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Preprint ARTICLE | doi:10.20944/preprints202010.0436.v1

Application of Machine Learning in Cricket and Predictive Analytics of IPL 2020

Anurag Sinha

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Naïve Bayes Classification; Eulers Strength Formula; Cricket Prediction; Supervised Learning; KNIME Tool; Cricket prediction; sports analytics; multivariate regression; neural network

Online: 21 October 2020 (12:34:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202102.0264.v1

SARS-CoV-2 and the Secret of the Furin Site

Antonio R. Romeu, Enric Ollé

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: SARS-CoV-2, RaTG13, Furin Site, Molecular Evolution, Bioinformatics.

Online: 10 February 2021 (15:46:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202005.0090.v1

The SIR and SEIR Epidemiological Models Revisited

John P. Maassen

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: epidemiological model; basic reproduction number; SIR; SEIR; COVID-19; stochas-tic model; Monte-Carlo simulation

Online: 6 May 2020 (03:15:02 CEST)

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Preprint REVIEW | doi:10.20944/preprints202001.0378.v1

Approaches for Containerized Scientific Workflows in Cloud Environments with Applications in Life Science

Ola Spjuth, Marco Capuccini, Matteo Carone, Anders Larsson, Wesley Schaal, Jon Ander Novella, Oliver Stein, Morgan Ekmefjord, Paolo Di Tommaso, Evan Floden, Cedric Notredame, Pablo Moreno, Payam Emami Khoonsari, Stephanie Herman, Kim Kultima, Samuel Lampa

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: workflows; containers; cloud computing; Kubernetes; big data; reproducibility

Online: 31 January 2020 (05:15:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints201812.0137.v1

Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction

Chinmay Belthangady, Loic A. Royer

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: microscopy, fluorescence, machine learning, deep learning, inverse problems, image reconstruction, image restoration, super-resolution, deconvolution, spectral unmixing

Online: 11 December 2018 (17:03:14 CET)

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Preprint REVIEW | doi:10.20944/preprints202104.0202.v1

A Brief Review on Spiking Neural Network - A Biological Inspiration

Taki Hasan Rafi

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Spiking Neural Network (SNN); Biological Inspiration; Deep Learning; Neuromorphic Computing

Online: 7 April 2021 (12:13:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202007.0369.v1

System for Control and Management of Data Privacy of Patients with COVID-19

Arielle Verri Lucca, Rodrigo Luchtenberg, Leonardo Garcez de Paula Conceicao, Luis Augusto Silva, Raúl García Ovejero, María Navarro-Cáceres, Valderi Reis Quietinho Leithardt

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Data privacy; Ambient intelligence; COVID-19

Online: 17 July 2020 (08:17:07 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201804.0352.v1

Integrative Approaches to Reconstruct Regulatory Networks From Multi-Omics Data: A Review of State-of-the-Art Methods

Nisar Wani, Khalid Raza

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: network inference; data integration; regulatory networks; transcription factor; gene expression

Online: 27 April 2018 (06:34:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201909.0139.v1

Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features

Talha Meraj, Arslan Hassan, Saliha Zahoor, Hafiz Tayyab Rauf, M.IkramUllah Lali, Liaqat Ali, Syed Ahmad Chan Bukhari

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: computer-aided detection (CAD) system\and computerized tomography (CT) scan; acquisition; segmentation; Classification and Principal Components Analysis (PCA)

Online: 14 September 2019 (18:25:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201909.0075.v1

Blood Cell Count Using Deep Learning Semantic Segmentation

Thanh Tran, Lam Binh Minh, Suk-Hwan Lee , Ki-Ryong Kwon

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Complete blood count; deep learning; segmentation; SegNet; Vgg-16

Online: 6 September 2019 (11:02:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202003.0344.v1

A Quantitative Genomic View of the Coronaviruses: SARS-COV2

Sk Sarif Hassan, Ranjeet Kumar Rout, Vipul Sharma

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: fractal dimension; Shannon entropy; Hurst Exponent; GC Content & SARS-CoV2

Online: 23 March 2020 (07:23:40 CET)

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Preprint REVIEW | doi:10.20944/preprints202110.0011.v1

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

Sameer Quazi

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Machine Learning; Precision Medicine; Genomic Medicine; Therapeutic; Artificial Intelligence

Online: 1 October 2021 (11:41:27 CEST)

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Preprint REVIEW | doi:10.20944/preprints202009.0757.v1

Mathematical Modelling in Prediction of Novel CoronaVirus (COVID-19) Transmission Dynamics

Md. Sharif Uddin, Md. Taufiq Nasseef, Mufti Mahmud, Ali AlArjani

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Coronavirus; ODE; SIR; SEIR; transmission dynamic; prediction; SARS-CoV-2

Online: 30 September 2020 (15:08:34 CEST)

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Preprint REVIEW | doi:10.20944/preprints202007.0123.v1

The Status of Causality in Biological Databases for Logical Modeling: Data Resources and Data Retrieval Possibilities

Vasundra Touré, Åsmund Flobak, Anna Niarakis, Steven Vercruysse, Martin Kuiper

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: causal interactions; databases; interoperability; biological pathway; logical modeling; computational biology

Online: 7 July 2020 (09:50:40 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202005.0401.v1

An Integrated In-Silico Approach to Develop Epitope-Based Peptide Vaccine against SARS-CoV-2

Prekshi Garg, Neha Srivastava, Prachi Srivastava

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: SARS-CoV-2; Peptide Vaccine; Spike Protein; Vaccinomics; Epitope Prediction

Online: 24 May 2020 (19:11:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201812.0137.v2

Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction

Chinmay Belthangady, Loic A. Royer

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: microscopy, fluorescence, machine learning, deep learning, inverse problems, image reconstruction, image restoration, super-resolution, deconvolution, spectral unmixing

Online: 5 February 2019 (10:30:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202005.0053.v1

Performance Analysis of Gaussian, Median, Mean and Weiner Filters on Biomedical Image De-Noising

Subrato Bharati, Prajoy Podder

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Gaussian noise; Speckle Noise; Mean square error(MSE); DE noising filters; Maximum difference value (MD); Peak signal to noise ratio(PSNR)

Online: 5 May 2020 (02:36:28 CEST)

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Preprint HYPOTHESIS | doi:10.20944/preprints202001.0133.v1

A Local Spherical Coordinate System Approach to Protein 3D Structure Description

Wei Li

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Cartesian Coordinate System; Spherical Coordinate System; Equilibrium Atomic Bond Length; Protein Structure Description; Protein Structure Tree-decomposition

Online: 12 January 2020 (17:23:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints201804.0333.v2

Stochastic Capsule Endoscopy Image Enhancement

Ahmed Mohammed, Ivar Farup, Marius Pedersen, Øistein Hovde, Sule Yildirim

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: capsule video endoscopy; stochastic sampling; random walks; color gradient; image decomposition

Online: 17 May 2018 (12:46:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202312.1770.v1

Shared Protentions in Multi-Agent Active Inference

Mahault Albarracin, Riddhi Jain Pitliya, Toby Benedict St. Clere Smithe, Daniel Ari Friedman, Karl Friston, Maxwell J. D. Ramstead

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: active inference; phenomenology; multi-agent; category theory

Online: 26 December 2023 (01:31:52 CET)

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Working Paper ARTICLE

When COVID-19 Will Decline in India? Prediction by Combination of Recovery and Case Load Rate

Atanu Bhattacharjee, Mukesh Kumar, Kamalesh Kumar Patel

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: COVID-19; recovery rate; case load rate; India

Online: 4 May 2020 (02:17:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202109.0425.v1

An Analysis of COVID-19 in Europe Based on Fractal Dimension and Meteorological Data

Cristina Maria Pacurar, Victor Dan Păcurar, Marius Paun

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Covid-19; fractal analysis; epidemic curves; box-counting dimension; reproduction rate; global radiation; daily mean temperature

Online: 24 September 2021 (11:19:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201804.0333.v1

Stochastic Capsule Endoscopy Image Enhancement

Ahmed Mohammed, Ivar Farup, Marius Pedersen, Øistein Hovde, Sule Yildirim

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: capsule video endoscopy; stochastic sampling; random walks; color gradient; image decomposition

Online: 26 April 2018 (08:01:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0632.v1

Mathematical Blockchain Modeling of Peer-to-Peer Networks for Gene Expression in Cell Population

Jung In Choi

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Blockchain; P2P Networks; Nucleobase DNA/RNA sequence; Gene Expression; Cell Population; Epigenetics; Methylation; Consensus; Hash Algorithm; Triple Codons; Protein Synthesis; Cryptocurrency

Online: 28 August 2020 (10:33:42 CEST)

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The study presents a mathematical modeling for supporting the premise that our body is composed of blockchain systems. A cell population is considered a space with peer-to-peer communication networks for applying blockchain protocol. Transaction is defined as gene expression that constantly occurs for protein synthesis in each cell. The transaction process proceeds according to the blockchain protocol with sharing and recording the data on the blockchain ledger. The premise comes from the clues of the previous research such that the cell population is a complex structured network system having cell-to-cell connections. Although individual cells exhibit stochastic nonlinear dynamic behavior, cell population shows consensus behavior that reaches to ensemble through interaction among cells. It is inferred that gene expression is not regulated by the corresponding cell only nor determined by external intelligence such as the brain. This is achieved through a consensus through stochastic interactions between cells over the whole cell population. These findings imply that mathematical blockchain modeling is a suitable for gene expression process. The original contribution of the study is a methodology for applying mathematically the blockchain protocol to the real biological gene expression process. In other words, the DNA sequence is converted into a numeral bit sequence that makes it possible to implement the blockchain protocol. A new DNA sequence scheme is proposed with adding methylated cytosine and adenine as the 5th and 6th bases for including epigenetic information which has profound effect on gene expression and regulation. The methodology was applied to the real biological synthesis process of protein samples. The protein is composed of amino acids that are encoded by triplet codons of 216 kinds with 6 base RNA sequence. The gene expression information is traced backward from a synthesized protein sample, amino acids of codons, RNA transcript up to DNA sequence. One of the results is a numeric value in the form of a bit sequence with which mathematical blockchain modeling is applicable. The cryptographic authentication and the consensus process are mathematically proven to work properly by the blockchain protocol. It implies that the same protein is synthesized, but with different epigenetic data, then protein's latent material properties will certainly be different. Although the result has not been justified by the biologic experiment yet, it is sure that the biological hidden algorithm inside DNA sequence will be revealed by the binary bit-logic with physical on/off states which is mathematically proven. The research will greatly contribute to disease treatment and medicine development as well as epigenetics in the future.

Preprint ARTICLE | doi:10.20944/preprints201804.0161.v1

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

Marco Abbatangelo, Estefanía Núñez-Carmona, Veronica Sberveglieri, Dario Zappa, Elisabetta Comini, Giorgio Sberveglieri

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: electronic nose; nanowire gas sensors; food quality control; Parmigiano Reggiano; multivariate data analysis; artificial neural network

Online: 12 April 2018 (06:25:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202109.0181.v1

A New Stacking Based Ensemble Predictive Model to Predict the Mortality after Heart Surgery on a Highly Imbalanced Dataset

Arman Ghavidel, Rouzbeh Ghousi, Alireza Atashi

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Classification; stacking ensemble method; heart surgery; unbalanced data problem; hybrid predictive model; machine learning in healthcare; resampling method; Edited-Nearest-Neighbor; nonparametric test.

Online: 10 September 2021 (10:53:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0313.v1

Estimating the Beginning and End Dates of Covid-19 for Several Countries Using the Logistic Model

Ademir Xavier Jr

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: covid-19; forecast; epidemic; logistic model; pandemic

Online: 19 April 2020 (02:21:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202001.0274.v1

Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine

Andrew M. K. Nassief

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: bioinformatics; computational genomics; computational medicine; data science; data visualization; parallel processing; grid computing; fog computing

Online: 24 January 2020 (10:26:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202012.0749.v2

SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation: A Dynamical Mathematical Model

José Díaz, Elena R. Álvarez-Buylla, Antonio Bensussen

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: SARS-CoV-2 infection; Interleukin 6; NFB; Nsp5; Cox2; SARS-CoV-2 interactome; Nonlinear dynamics of inflammation

Online: 15 March 2021 (12:18:02 CET)

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Working Paper ARTICLE

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Robert Ancuceanu, Bogdan Tamba, Cristina Silvia Stoicescu, Mihaela Dinu

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: c-src-tyrosine kinase; QSAR; molecular descriptors; virtual screening; drug discovery; cancer; molecular docking

Online: 25 October 2019 (11:21:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201909.0171.v1

Amplicon Analysis I

Eduardo Pareja-Tobes, Raquel Tobes

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Bioinformatics; NGS; DNA sequencing; DNA sequence analysis; amplicons; ultrametric spaces; indexing

Online: 16 September 2019 (16:45:10 CEST)

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Preprint REVIEW | doi:10.20944/preprints202305.0663.v1

Depression detection model using multimodal deep learning

Hyewon Yoo, Hayoung Oh

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: depression detection; fusion; feature extraction; deep learning

Online: 9 May 2023 (13:20:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202203.0252.v1

Leveraging Immersive Technologies to Support Blended Learning Post Covid-19

Simon Colreavy-Donnelly, Alan Ryan, Stuart O'Connor, Fabio Caraffini, Stefan Kuhn, Salim Hasshu

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Audio-Visual Technologies; Blended Learning; Pedagogy; Virtual Learning Environments; Virtual Reality

Online: 17 March 2022 (11:05:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints201904.0077.v1

Evaluating Approximations and Heuristic Measures of Integrated Information

André Sevenius Nilsen, Bjørn Erik Juel, William Marshall, Johan Frederik Storm

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: integrated information theory; differentiation; integration; complexity; consciousness; computational; IIT; Phi

Online: 8 April 2019 (08:58:29 CEST)

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Integrated information theory (IIT) proposes a measure of integrated information (Φ) to capture the level of consciousness for a physical system in a given state. Unfortunately, calculating Φ itself is currently only possible for very small model systems, and far from computable for the kinds of systems typically associated with consciousness (brains). Here, we consider several proposed measures and computational approximations, some of which can be applied to larger systems, and test if they correlate well with Φ. While these measures and approximations capture intuitions underlying IIT and some have had success in practical applications, it has not been shown that they actually quantify the type of integrated information specified by the latest version of IIT. In this study, we evaluated these approximations and heuristic measures, based not on practical or clinical considerations, but rather based on how well they estimate the Φ values of model systems. To do this, we simulated networks consisting of 3–6 binary linear threshold nodes randomly connected with excitatory and inhibitory connections. For each system, we then constructed the system’s state transition probability matrix (TPM), as well as its state transition matrix (STM) over time for all possible initial states. From these matrices, we calculated, approximations to Φ, and measures based on state differentiation, state entropy, state uniqueness, and integrated information. All measures were correlated with Φ in a state dependent and state independent manner. Our findings suggest that Φ can be approximated closely in small binary systems by using one or more of the readily available approximations (r > 0.95), but without major reductions in computational demands. Furthermore, Φ correlated strongly with measures of signal complexity (LZ, r_s= 0.722), decoder based integrated information (Φ*, r_s= 0.816), and state differentiation (D1, r_s= 0.827), on the system level (state independent). These measures could allow for efficient estimation of Φ on a group level, or as accurate predictors of low, but not high, Φ systems. While it’s uncertain whether the results extend to larger systems or systems with other dynamics, we stress the importance that measures aimed at being practical alternatives to Φ are at a minimum rigorously tested in an environment where the ground truth can be established.

Preprint ARTICLE | doi:10.20944/preprints202007.0037.v1

Ultrametric Model for COVID-19 Dynamics: An Attempt to Explain Slow Approaching Herd Immunity in Sweden

Andrei Khrennikov

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: covid-19; epidemy; infection spreading; herd immunity; hierarchy of social clusters; energy landscapes; random walk; trees; ultarmetric spaces; social barriers; linear growing barrier

Online: 3 July 2020 (12:17:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201812.0184.v1

HoSeIn: A Workflow for Integrating Various Homology Search Results from a High-Throughput Sequence Dataset

Christina McCarthy, Gastón Rozadilla, Jorgelina Moreiras Clemente

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Metagenomics; Metatranscriptomics; Environmental sample; Homology searches; Taxonomic profile; Functional profile

Online: 17 December 2018 (09:51:22 CET)

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Working Paper HYPOTHESIS

A Hybrid Model of the Cerebellum: An Overview

Mike Gilbert, R. Chris Miall

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: cerebellum; network; model; circuit; theory

Online: 6 February 2020 (04:02:21 CET)

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Preprint ARTICLE | doi:10.20944/preprints202303.0037.v1

PHA4GE Quality Control Contextual Data Tags: Standardized Annotations for Sharing Public Health Sequence Datasets with Known Quality Issues to Facilitate Testing and Training

Emma Griffiths, Catarina Inês Mendes, Finlay Maguire, Jennifer Guthrie, Leonid Chindelevitch, Ilene Karsch-Mizrachi, Zahra Waheed, Rhiannon Cameron, Kathryn Holt, Lee Katz, Robert Petit III, Duncan MacCannell, Mugdha Dave, Paul Oluniyi, Muhammad Ibtisam Nasar, Amogelang Raphenya, William Hsiao, Ruth Timme

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: quality control; reference datasets; public health; metadata; data sharing

Online: 2 March 2023 (07:50:05 CET)

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As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower-quality datasets be made available for analysis and comparison alongside those of higher-quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies, community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA’s GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.

Preprint CONCEPT PAPER | doi:10.20944/preprints202204.0104.v1

Statistical Modelling and Experimental Design for the Validation of Droplet Digital PCR Methods

Steffen Uhlig, Christopher Weidner, Bertrand Colson

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Method validation; droplet digital PCR; orthogonal factorial design; variance components; Poisson assumption; cloglog model; target DNA copies per droplet; Monte Carlo; prediction interval

Online: 12 April 2022 (08:46:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201809.0507.v1

BioSearch Engine Design : R Code elucidation into the mechanics of search engine that reveals unexplored/ untested combinatorial biological hypotheses in silico, using ETC-1922159 data.†

Shriprakash Sinha

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: BioSearch Engine Designm, ETC-1922159, Ranking, Combinatorial forest space, Sensitivity analysis, Support Vector Machine, Colorectal Cancer

Online: 26 September 2018 (10:59:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0061.v1

Developing Computational Thinking to Help Tackle Pandemic Challenges

Roberto Araya, Masami Isoda, Johan van der Molen Morris

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: COVID-19; Computational Thinking; Computational Modeling; Lesson Study

Online: 4 October 2021 (14:37:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0377.v1

Wireless Body Area Network and Sensor Data Interoperability

Joy Alatta

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Sensor data, wireless body area network, wearable devices, sensor data interoperability

Online: 17 May 2021 (09:47:26 CEST)

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Preprint REVIEW | doi:10.20944/preprints202110.0185.v1

Expanding Interactome Analyses beyond Model Eukaryotes

Katherine James, Anil Wipat, Simon Cockell

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: systems biology; integrative bioinformatics; interactomics; eukaryotes; non-models; network biology

Online: 12 October 2021 (14:24:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0080.v1

A Weakly Supervised Tissue-level Approach for Histological Differentiation of Gastric Adenocarcinomas

Dong-Woo Hyeon, Dongmin Kim, Wi-Sun Ryu, Samuel Hanjoo Sung, Subrata Bhattacharjee, Cho-Hee Kim, Nam-Hun Cho, Damin Moon, Doo-Hee Lee, Hyunki Kim, Hyeong-Chan Shin, Won-Ae Lee, Min-Sun Cho, Heung-Kook Choi, Haeyoun Kang

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: gastric cancer; deep learning; digital pathology; lymph node metastasis

Online: 3 August 2022 (08:48:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202202.0051.v1

Machine Learning based Survival Group Prediction in Glioblastoma

Manasa Kalya, Alexander Kel, Andreas Leha, Kamilya Altynbekova, Edgar Wingender, Tim beissbarth

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Glioblastoma; survival prediction; Machine Learning; biomarkers; HumanPSDTM; Long-term survivor

Online: 3 February 2022 (12:00:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202008.0221.v1

Evaluation of the Severity of Major Depression Using a Voice Index for Emotional Arousal

Shuji Shinohara, Hiroyuki Toda, Mitsuteru Nakamura, Yasuhiro Omiya, Masakazu Higuchi, Takeshi Takano, Taku Saito, Masaaki Tanichi, Shuken Boku, Shunji Mitsuyoshi, Mirai So, Aihide Yoshino, Shinich Tokuno

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: arousal level; emotion; major depression severity; voice index; Hurst exponent; zero-crossing rate; Hamilton Rating Scale for Depression

Online: 9 August 2020 (21:15:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0355.v1

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules From Their Mass Spectra

Aditya Divyakant Shrivastava, Neil Swainston, Soumitra Samanta, Ivayla Roberts, Marina Wright Muelas, Douglas Bruce Kell

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Deep Learning; Electrospray; Mass Spectrometry; Metabolomics; Artificial Intelligence; Generative Methods; Chemical Space; Transformers

Online: 25 October 2021 (13:20:47 CEST)

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The ‘inverse problem’ of mass spectrometric molecular identification (‘given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came’) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem (‘calculate a small molecule’s likely fragmentation and hence at least some of its mass spectrum from its structure alone’) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the ‘translation’ a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the ‘true’ molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are ‘similar’ to the top hit. In addition to using the ‘top hits’ directly, we can produce a rank order of these by ‘round-tripping’ candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to ‘learn’ millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

Preprint ARTICLE | doi:10.20944/preprints202012.0749.v1

SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation: A Mathematical Model

José Díaz, Elena R. Álvarez-Buylla, Antonio Bensussen

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: SARS-CoV-2 infection; Interleukin 6; NFB; Nsp5; Cox2; SARS-CoV-2 interactome; Nonlinear dynamics of inflammation

Online: 30 December 2020 (09:42:41 CET)

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Preprint ARTICLE | doi:10.20944/preprints201910.0113.v1

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Robert Ancuceanu, Bogdan Tamba, Cristina Silvia Stoicescu, Mihaela Dinu

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: QSAR; c-src; tyrosine kinase; docking; virtual screening

Online: 10 October 2019 (07:40:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201910.0360.v1

A High Performance Computing Implementation of Iterative Random Forest for the Creation of Predictive Expression Networks

Ashley Cliff, Jonathon Romero, David Kainer, Daniel Jacobson

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Random Forest; Iterative Random Forest; gene expression networks; high performance computing; X-AI-based eQTL

Online: 31 October 2019 (02:33:17 CET)

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Preprint REVIEW | doi:10.20944/preprints202111.0448.v1

Modulating Visuomotor Sequence Learning by Repetitive Transcranial Magnetic Stimulation: What Do We Know So Far?

Laura Szucs-Bencze, Teodora Vekony, Orsolya Pesthy, Nikoletta Szabo, Zsigmond Tamas Kincses, Zsolt Turi, Dezso Nemeth

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: non-invasive brain stimulation; sequence learning; statistical learning; repetitive TMS; DLPFC

Online: 24 November 2021 (09:18:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202304.0320.v1

Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks

Ashwini Kodipalli, Vaishnavi Gururaj, Shriya VR, Steven L Fernandes, Santosh Dasar

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Ovarian Tumours; UNet; Convolutional Neural Networks; VGG 16; DenseNet; ResNet; Dice score; Jaccard score

Online: 13 April 2023 (10:50:53 CEST)

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Preprint REVIEW | doi:10.20944/preprints202201.0170.v1

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

Lionel Alangeh Ngobesing, Yılmaz Atay

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: significative infrastructure; biological networks; normalized mutual information; recall; pre-cision; modularity; gene ontology

Online: 12 January 2022 (14:17:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202007.0622.v1

CausalBuilder: Bringing the MI2CAST Causal Interaction Annotation Standard to the Curator

Vasundra Touré, John Zobolas, Martin Kuiper, Steven Vercruysse

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: causal statement; metadata; curation guidelines; curation web interface; VSM; MI2CAST

Online: 25 July 2020 (18:24:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0320.v1

Ten Simple Rules for Using Public Data for Your Research

Vishal Oza, Jordan Whitlock, Elizabeth Wilk, Angelina Uno-Antonison, Brandon Wilk, Manavalan Gajapathy, Timothy Howton, Austyn Trull, Lara Ianov, Elizabeth Worthey, Brittany Lasseigne

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: data; reproducibility; FAIR; data reuse; public data; big data; analysis

Online: 23 June 2022 (08:06:50 CEST)

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Preprint BRIEF REPORT | doi:10.20944/preprints202209.0172.v1

NOMA-DB: A Framework for Management and Analysis of Ageing-related Gene-Expression Data

Pietro Hiram Guzzi, Rocco Scicchitano, Pierangelo Veltri

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: ageing GTEx analysis

Online: 13 September 2022 (10:36:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0661.v1

Partial Information Decomposition and the Information Delta: A Geometric Unification Disentangling Non-Pairwise Information

James Kunert-Graf, Nikita Sakhanenko, David Galas

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Partial Information Decomposition; Information Delta; Synergy; Co-Information; Non-Pairwise Dependence

Online: 27 September 2020 (04:18:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.1745.v1

Optimized FIR Filter using Genetic Algorithms: A Case Study of ECG Signals Filter Optimization

Houssam Hamici, Awos Kanan, Khalid Al-hammuri

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Genetic Algorithms; Digital Filters; Metaheuristic Optimization; Finite Impulse Response (FIR); Side Lobe Level (SLL).

Online: 24 August 2023 (10:46:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202303.0122.v1

Automatic Generation of SBML Kinetic Models from Natural Language Texts using GPT

Kazuhiro Maeda, Hiroyuki Kurata

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: GPT; language model; kinetic modeling; simulation; systems biology; natural language processing

Online: 7 March 2023 (02:59:59 CET)

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Preprint BRIEF REPORT | doi:10.20944/preprints202110.0080.v1

SARS-CoV-2 and MERS-CoV Share the Furin Site CGG-CGG Genetic Footprint

Antonio R. Romeu

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: SARS-CoV-2; MERS-CoV; arginine dimer; polybasic furin cleavage site; aginine codon usage; Markov model; bioinformatics

Online: 5 October 2021 (11:26:34 CEST)

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Preprint REVIEW | doi:10.20944/preprints202009.0652.v3

Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles

Paul Dulaud, Ines Di Loreto, Denis Mottet

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: quantified self; health; physical activity; behavior change; model; support system; persuasive design; user centered design

Online: 7 December 2020 (10:53:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202207.0156.v1

A Novel Method for Lung Image Processing Using Complex Networks

Laura Broască, Ana Adriana Trușculescu, Versavia Maria Ancușa, Horia Ciocârlie, Cristian-Iulian Oancea, Emil-Robert Stoicescu, Diana Luminița Manolescu

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: diffuse interstitial lung disease; complex networks; model; HRCT

Online: 11 July 2022 (09:12:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0413.v1

A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning

Vincent Schilling, Peter Beyerlein, Jeremy Chien

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: ovarian cancer; machine learning; SHAP; diagnostic biomarkers; platinum resistance

Online: 6 May 2023 (10:24:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0031.v1

Adaptive Badging Control for A Decentralized Epidemic Policyusing Feasible Group Testing Protocol (Abcdefg Protocol)

Inavamsi Enaganti, Shirshendu Chatterjee, Bud Mishra

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Epidemics; Badging, Pool-testing; Group-testing; ODE model; Policies

Online: 6 April 2022 (05:40:04 CEST)

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The ABCDEFG Protocol provides an evolving tool for imposing structure on the flow of Covid infection information obtained from community testing, collective policy and individual compliance. ABCDEFG Protocol could not assume soundness, invariance, symmetry and completeness of the available information and relied on signaling game theory to design solutions that could evolve with the variable narratives, theories, individual utilities and pathogen variants. Thus, ABCDEFG Protocol suggests a novel and a very flexible pool-testing and badging protocol in the context of controlling contagious epidemics and tackling the far-reaching associated challenges, including understanding and evaluating individual and collective risks of returning prior infected individuals to normal society and other economic and social arrangements and interventions to protect against disease. AbCDEFG Proto uses both control theoretic and game theoretic mathematical models that may be centralized (an optimizing policy maker mandates behavior based on estimated models) or decentralized (a strategizing individual selects their behavior based on available asymmetric information). ABCDEFG protocol demonstrates how society can continue to carry out plausible economic activities in addition to controlling the prevalence of a contagious disease by keeping the number of infected people below a desired limit without compromising an individuals' privacy despite the presence of deception and selfishness among people, and limitations of available resources. Different types of badges would come with different restrictions. Badges would be reissued periodically by third-party testing centers via suitably frequent pool testing of samples of the participants. The size of the pools, frequency of tests, and allowable activities for people with a given type of badge would depend on the available resources, the prevalence of the disease, and the efficacy of the equipment used in the tests.

Preprint ARTICLE | doi:10.20944/preprints202211.0546.v1

All-cause Excess Mortality and Confirmed COVID-19 Deaths in Italy: A Peak Comparison Analysis

Marco Roccetti

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: COVID-19; pandemic; Italy; daily confirmed deaths; all-cause excess mortality; peak comparison; public health; epidemiology; health informatics

Online: 29 November 2022 (09:18:41 CET)

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Preprint ARTICLE | doi:10.20944/preprints202306.0010.v1

CoVigator – a Knowledge Base for Navigating SARS-CoV-2 Genomic Variants

Thomas Bukur, Pablo Riesgo-Ferreiro, Patrick Sorn, Ranganath Gudimella, Johannes Hausmann, Thomas Rösler, Martin Löwer, Barbara Schrörs, Ugur Sahin

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: SARS-CoV-2; dashboard; genomic variants; software; pipeline; virus genome assemblies; knowledge base

Online: 1 June 2023 (03:16:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0352.v1

Hopf Bifurcation Analysis and Optimal Control of an Infectious Disease with Awareness Campaign and Treatment

Fahad Al Basir, Biru Rajak, Bootan M. Rahman, Khalid Hattaf

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Mathematical model; Basic reproduction number; Stability theory; Forward bifurcation; Minimum principle; Numerical simulations

Online: 5 May 2023 (10:40:07 CEST)

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Preprint REVIEW | doi:10.20944/preprints202009.0652.v2

Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles

Paul Dulaud, Ines Di Loreto, Denis Mottet

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: quantified self; health; physical activity; behavior change; model; support system; persuasive design; user centered design

Online: 24 November 2020 (09:59:07 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.1289.v1

The Use of Graph Databases in Systems Biology: A Systematic Review

Ilya Mazein, Adrien Rougny, Alexander Mazein, Ron Henkel, Lea Gütebier, Lea Michaelis, Marek Ostaszewski, Reinhard Schneider, Venkata Satagopam, Lars Jensen, Dagmar Waltemath, Judith Wodke, Irina Balaur

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: graph databases; RDF; NoSQL databases; systems biology; network biology; ontology

Online: 21 March 2024 (13:52:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0472.v1

Feeling Our Place in the World: An Active Inference Account of Self-Esteem

Mahault Albarracin, Gabriel Bouchard-Joly, Zara Sheikhbahaee, Mark Miller, Riddhi J. Pitliya, Pierre Poirier

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: self-esteem; active inference; model; sociometer

Online: 8 February 2024 (07:10:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202306.1773.v1

Analyzing a SEIR-Type Mathematical Model of SARS-COVID-19 Using Piecewise Fractional Order Operators

Nadiyah Hussain Alharthi, Mdi Begum Jeelani

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Dynamical system; Piecewise derivative; Newton polynomials; Fractional order iterative method

Online: 26 June 2023 (09:46:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0408.v1

A Machine Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae

Leone Ermes Romano, Maurizio Iovane, Luigi Gennaro Izzo, Giovanna Aronne

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Duckweed; Machine learning; Image analysis; Machine training; Aquatic plants; Lemnaceae; Lemna

Online: 29 June 2022 (14:56:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.1012.v1

Graph-Theoretical Analysis of Biological Networks

Kayhan Erciyes

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: biological network; graph analysis; clustering; network motif; network alignment

Online: 14 July 2023 (13:22:33 CEST)

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Preprint BRIEF REPORT | doi:10.20944/preprints202303.0207.v2

Sex Age Annotated Omics Data: Enabling Powerful Omics Studies

Pietro Hiram Guzzi, Francesca Cortese, Raffaele Giancotti, Ugo Lomoio, Barbara Puccio, Patrizia Vizza, Giuseppe Tradigo, Pierangelo Veltri

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Annotation; Biological Databases; Aging Analysis

Online: 15 March 2023 (02:00:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202207.0453.v1

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-optimization

Stefan Michael Kohlbacher, Matthias Schmid, Thomas Seidel, Thierry Langer

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: pharmacophore; pharmacophore modelling; quantitative pharmacophore; QSAR; machine learning; pharmacophore optimization; NeuroDeRisk

Online: 29 July 2022 (08:47:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202303.0328.v1

Neutronic Assessments towards a Novel First Wall Design for a Stellarator Fusion Reactor with Dual Coolant Lithium Lead Breeding Blanket

David Sosa, Iole Palermo

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Fusion; DCLL; Breeding Blanket; HELIAS; TBR; neutronic

Online: 20 March 2023 (01:08:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202207.0131.v2

Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings

Samuel Boudet, Agathe Houzé de l'Aulnoit, Laurent Peyrodie, Romain Demailly, Denis Houzé de l'Aulnoit

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: fetal heart rate; maternal heart rate; cardiotocogram; gated recurrent unit; deep learning

Online: 22 July 2022 (03:08:59 CEST)

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We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.

Preprint REVIEW | doi:10.20944/preprints202403.0120.v1

A Review for Artificial Intelligence Based Protein Subcellular Localization

Hanyu Xiao, Yijin Zou, Jieqiong Wang, Shibiao Wan

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Protein Subcellular Localization; Machine Learning; Deep Learning; Artificial Intelligence; Gene Ontology; Sequence Analysis

Online: 4 March 2024 (05:55:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0640.v1

Comparing the Efficacy of Two Generations of EGFR-TKIs: An Integrated Drug-Disease Mechanistic Model Approach in Egfr-Mutant Lung Adenocarcinoma

Hippolyte Darré, Perrine Masson, Arnaud Nativel, Laura Villain, Diane Lefaudeux, Claire Couty, Bastien Martin, Evgueni Jacob, Michaël Duruisseaux, Jean-Louis Palgen, Claudio Monteiro, Adèle L’Hostis

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Lung adenocarcinoma; EGFR TKI; precision oncology; in silico; computational oncology

Online: 12 February 2024 (10:57:20 CET)

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Mutations in epidermal growth factor receptor (EGFR) occur in about 48% of Asian and 19% of Western patients with lung adenocarcinoma (LUAD), leading to its constitutive activation and subsequent uncontrolled cell proliferation. To counteract this, tyrosine kinase inhibitors (TKIs) have been developed, targeting EGFR activity. However, the emergence of metastases and resistance mutations often undermines the durability of the treatment response. Knowledge-based mechanistic models that replicate existing clinical trial outcomes, tailored to specific population characteristics, provide invaluable assistance in designing future clinical trials. These models are particularly effective in identifying optimal responders to specific treatments, thereby enhancing therapeutic efficacy. We have developed physiologically based pharmacokinetic (PBPK) models for gefitinib and osimertinib, representing two distinct generations of EGFR TKIs, to accurately simulate the drugs’ distribution within the primary tumor and metastatic sites following oral administration. These models were integrated with a pathophysiological mechanistic model of EGFR-mutant LUAD, enabling the representation of the effects of both gefitinib and osimertinib on EGFR activation-induced signaling pathways. This comprehensive model outputs detailed evolutions of the primary tumor and each metastatic site, facilitating the evaluation of patient progression in alignment with RECIST guidelines (version 1.1). Notably, the model encapsulates the heterogeneity within the tumor, through the representation of various subclones, each characterized by unique mutation profiles, thereby reflecting differential responses to treatment. Calibration of the model was achieved using publicly available data from the NEJ002 and FLAURA clinical trials. Rigorous visual predictive checks and statistical tests were employed to ensure the proper behavior of the model. The model adeptly replicated the time to progression observed in real clinical trials in EGFR-mutant LUAD patients receiving gefitinib or osimertinib as first-line therapy. In addition, the model was able to reproduce the results from a retrospective study comparing gefitinib and osimertinib, a study that was not used to inform the model. With a consistent virtual population as a reference, the combined model facilitated a comparative analysis of the efficacy of both treatments, thereby underscoring its utility in evaluating therapeutic strategies. The faithful replication of real-world data significantly enhances the credibility of our model, rendering it a vital investigational tool for deriving pertinent insights and informing treatment strategies, particularly in identifying optimal responders to various generations of EGFR TKIs. Following its consecutive prospective validations in the FLAURA2 and MARIPOSA trials, the model is poised to facilitate the generation of synthetic control arms in future clinical trials. This advancement promises a more nuanced analysis of covariate relationships, especially when comparing investigational treatments to established standards of care. Additionally, it potentially reduces the required patient cohort size in such trials, optimizing resource utilization.

Preprint ARTICLE | doi:10.20944/preprints202309.0208.v1

Evaluation of Coproporphyrins I and III as an Endogenous Marker for Hepatic Organic Anion Transporting Polypeptides and Multidrug Resistance-Associated Proteins (MRP 2/3) Activity in Animal Models of Nonalcoholic Steatohepatitis Using a Mechanistic Model

Rajeev Kumar, Sagnik Chatterjee, Ravindranath Reddy Gilibili, T Thanga Mariappam, Hong Shen

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Coproporphyrin; Oatps; Mrp; Endogenous biomarker; Modeling and Simulation

Online: 5 September 2023 (03:40:58 CEST)

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Coproporphyrins I and III (CP-I and CP-III) are established as substrates of hepatic sinusoidal organic anion transporting polypeptides (Oatps), multidrug resistance-associated protein 3 (Mrp3), and canalicular transporter Mrp2. We aimed to evaluate the potential of plasma CP-I and CP-III concentrations to indicate functional changes in hepatic transporters in nonalcoholic stea-tohepatitis (NASH) rodent models using modeling and simulation approach. Mechanistic copro-porphyrins (CP-I and CP-III) models in rats and mice were constructed based on metabolism, transport, and elimination pathways in rodents. To parameterize the rat mechanistic model, we measured CP concentrations in plasma, liver, bile, and urine in healthy control Sprague Dawley rats. The mouse mechanistic model was parameterized using published data. The rat and mouse CP models described the steady-state levels of CP-I and CP-III in plasma, liver, and bile. The model recapitulated the observed high plasma CP-III / CP-1 ratio (9- and 3-fold in rats and mice, respec-tively) and elimination pathways of CP-I (>90% via biliary secretion) and CP-III (equal via bile and urine). Rat CP model was further validated with the published rat fistula model. The simulated increased plasma CP-I concentrations (4-fold), resulted from decreased Oatp activity (0.6-fold) and increased Mrp3 activity (3-fold) were comparable to those observed in bile-duct ligated NASH model. Sensitivity analysis revealed that 90% inhibition of Oatp and Mrp2 leads to a 4-fold and 2.5-fold increase in plasma CP-I levels, respectively. Additionally, we demonstrated the utility of the mechanistic CP models in testing hypothesis and understanding the mechanisms of the ob-served plasma CPs disposition in the Oatp knockout (KO) mice, bile-duct ligation NASH mice, and Mrp2 deficient TR- rats. Collectively, using a mechanistic model, we conclude that CP-I is a robust and predictable marker for assessing change in the activity of Oatps and Mrps in animal models. Our analysis suggests that efflux transporters (Mrp2 and Mrp3) can play an important role in the disposition of CP-I along with Oatp transporters in rodents.

Preprint ARTICLE | doi:10.20944/preprints202308.0212.v1

Atangana-Baleanu fractional dynamics of predictive whooping cough model with optimal control analysis

Azhar Iqbal Kashif Butt

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: whooping cough; atangana-Baleanu derivative; vaccination; existence and uniqueness; stability and sensitivity analysis; toufik-atangana scheme; optimal control

Online: 3 August 2023 (08:28:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.0076.v1

A Theoretical Analysis of the Differential Chemical Reaction Results Caused by Chirality Induction

Feng-Yu Zhang, Si-Cheng Liu, Anwei Huang, Yi-Ning Li, Xiao-Yan Liu, Peng Zhang

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Chiral drugs; Chirality-induced spin selectivity (CISS); Chiral enzymes; L-DOPA; AADC enzyme

Online: 1 August 2023 (10:40:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1743.v1

Aggravation of Health Problems Subsequent to COVID-19 Lockdown

Fatma Nese Efil, Sania Qureshi, Nezihal Gokbulut, Evren Hınçal, Amanullah Soomro

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Covid-19; mathematical modeling; cancer; diabetes; heart diseases; sensitivity analysis

Online: 26 September 2023 (10:40:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.0105.v1

Analysis and Optimal Control Measures of a Typhoid Fever Mathematical Model for a Multiple Socio-Economic Population

Stephen Ekwueme Ekwueme, Obiora Cornelius Collins, Ifeanyi Sunday Onah

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: typhoid fever; reproduction number; stability analysis; optimal control; numerical analysis

Online: 3 October 2023 (08:48:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202311.0242.v1

A New Mixed Fractional Derivative with Application to Computational Biology

Khalid Hattaf

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Fractional operators; singular and non-singular kernels; Laplace transform; numerical method

Online: 3 November 2023 (11:03:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202307.0399.v1

Can Plants Sense Humans? - Using Plants as Biosensors to Detect the Presence of Eurythmic Gestures

Luis De la Cal Garcia, Peter A. Gloor, Moritz Weinbeer

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Biosensors; Machine-Learning; Motion detection; Plant-Human interaction

Online: 6 July 2023 (10:13:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.1185.v1

Genetic regulation of human isomiR biogenesis

Guanglong Jiang, Jill L. Reiter, Chuanpeng Dong, Yue Wang, Fang Fang, Zhaoyang Jiang, Yunlong Liu

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: microRNA; isomiR; genetic association

Online: 18 July 2023 (07:52:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.0848.v1

Graph-Root: Prediction of Root-Associated Proteins in Plants Based on Graph Convolutional Network and Network Embedding Method

Lei Chen, Siyang Liu

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Root-related proteins; Deep learning; Graph convolutional network; Multi-head attention; Network embedding

Online: 12 July 2023 (12:41:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202311.0245.v1

Graph Propagation Dynamics of an Epidemic Model with Heterogeneous Control Strategies on Complex Networks

Yan Wang, Shanshan Chen, Dingguo Yu, Lixiang Liu, Ke-ke Shang

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Complex network; Epidemic threshold; Global stability; Control Strategies

Online: 3 November 2023 (11:40:19 CET)

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Preprint ARTICLE | doi:10.20944/preprints202306.0469.v1

Dynamical Analysis of an Age-Structured Sveir Model With Imperfect Vaccine

Yanshu Wang, Hailiang Zhang

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: SVEIR model; latent age; imperfect vaccine; Lyapunov functions; stability

Online: 7 June 2023 (02:32:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202401.0095.v1

sRNAflow: A Tool for Analysis of Small RNA-Seq Data

Pawel Zayakin

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: bioinformatics; small RNA; microbiome; non-coding RNA; biofluids; miRNA; isomiR; tRF; biomarker; cancer biology

Online: 3 January 2024 (10:26:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202308.1037.v1

A novel dynamic model describing the spread of virus

Veli Shakhmurov, Muhammet Kurulay, Aida Sahmurova, Mustafa Can Gursesli, Antonio Lanata

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Mathematical modeling; Virus; Immune system; Stability of dynamical systems; SEIR

Online: 14 August 2023 (11:13:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.1338.v1

Is Drug Delivery System a Deterministic or Probabilistic Approach? A Theoretical Model Based in the Sequence Electrodynamics-Diffusion-Bayes

Huber Nieto-Chaupis

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: drug delivery; bayes; diffusion equation

Online: 18 August 2023 (07:42:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.2003.v1

Covid-19: From Limit Cycle to Stable Focus

Alexander V. Sokolov, Vladimir V. Voloshinov

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: epidemiology; morbidity; immunity; equilibrium; bifurcation

Online: 29 June 2023 (02:09:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.0092.v1

Models of Protocells Undergoing Asymmetrical Division

Marco Villani, Elena Alboresi, Roberto Serra

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: protocell; synchronization; asymmetric replication; populations; models; sustained reproduction

Online: 2 February 2024 (07:30:31 CET)

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Preprint REVIEW | doi:10.20944/preprints202404.0055.v1

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Sarfaraz K. Niazi, Zamara Mariam, Matthias Magoola

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Lead optimization; drug discovery; artificial intelligence; machine learning; deep neural networks; cheminformatics; chemical modifications; drug development

Online: 1 April 2024 (13:35:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.1868.v1

Pattern Formation in a Predator-Prey Model with Allee Effect and Hyperbolic Mortality on Multiplex Networks

Lei Shi, Jiaying Zhou, Yong Ye

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Turing pattern; predator-prey model; multiplex networks; spatiotemporal pattern

Online: 27 June 2023 (10:43:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1530.v4

Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics

Alexandre S. Queiroga, Mauro Cesar Cafundó Morais, Beatriz Stransky

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: cells co-culture; population dynamic; cellular automata; boolean network; basin of attraction

Online: 7 November 2023 (12:50:45 CET)

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Human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. As in dynamic system concepts, one can consider cluster formation as the system attractor, cell seeding as the initial condition, and density change over time as a path within the basin of attraction. Herein, Cellular Automata, which is a class of Agent-Based Models, and Boolean Networks are discrete modeling methods used in population dynamics such that running the dynamics of cellular automata backward reveals an underlying network in terms of basin of attraction, also known as global dynamics. Thus, we hypothesize that one can estimate the local dynamic of an agent-based model from the basin of attraction of a boolean network. Here, we propose an approach to estimating these agent-based model rules, which consists of comparing the density states within each state transition and reaching a consensus among state transitions belonging to a basin of attraction of a boolean network. The objectives of this study are: (1) to infer a boolean network from the co-culture growth curve; (2) to estimate the rules of agent-based models; and (3) to implement spatial dynamics simulations. The binarization of the growth curve shows high population density after four days; the estimated agent-based model rules were compatible with cell proliferation and migration in agreement with literature, so we had to propose individual rules for survival and death. Spatial dynamics shows that (1) keratinocytes exhibit a higher density in neighborhoods where melanoma is present; (2) the chance of keratinocyte migration increases until the fourth day, then decreases, and the probability of survival increases substantially after four days; and (3) cells with low proliferation capacity die and free space for those with high ones. Our approach suggests that the attractor state of cell co-culture is induced mainly by an increase in keratinocyte migration and survival. Our approach has the potential to offer valuable clues about microenvironmental interactions or configurations that drive population dynamics.

Preprint ARTICLE | doi:10.20944/preprints202308.0677.v1

Using Kernel Density Estimation in Knowledge Distillation to Construct the Prediction Model for Bipolar Disorder Patients

Yu-Shiang Tseng, Meng-Han Yang

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: bipolar disorder; knowledge distillation; kernel density estimation; Medical Information Mart for Intensive Care (MIMIC); decision tree; artificial neural network

Online: 9 August 2023 (05:38:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.0814.v1

Evaluating Efficiency of the US Surveillance Systems for Monitoring Antimicrobial-Resistant Gonorrhea: An Agent-Based Modelling Study

Sofya Prakhova

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Agent-based modelling; antimicrobial-resistant gonorrhea; surveillance systems

Online: 12 October 2023 (11:24:24 CEST)

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Computer Science and Mathematics