Containers are gaining popularity in life science research as they provide a solution for encompassing dependencies of provisioned tools, simplify software installations for end users and offer a form of isolation between processes. Scientific workflows are ideal for chaining containers into data analysis pipelines to aid in creating reproducible analyses. In this manuscript we review a number of approaches to using containers as implemented in the workflow tools Nextflow, Galaxy, Pachyderm, Argo, Kubeflow, Luigi and SciPipe, when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

To date, Cartesian (x, y, z) coordinate system (CCS) has been the default approach to geometrically specify atomic spatial positions in protein structures since the launch of Protein Data Bank (PDB) in 1971. To this end, this paper proposes a local spherical coordinate system (SCS) approach as an alternative to the default approach and a previously reported global SCS approach. The local SCS approach here requires only two parameters (θ and φ), instead of x, y and z as required by the default CCS approach. Essentially, CCS and SCS are like the two sides of one coin, i.e., geometric coordinate system for three-dimensional position specification. Therefore, this paper furthermore argues that it is time to flip the coin over, and have a look at the other side of the coin, e.g., the local SCS approach, which possesses an intrinsically lower degree of descriptional complexity than that of the default CCS approach, and constitutes a potentially useful alternative perspective for all protein structural research field.

As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high performance computing(HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers Summit and Titan. We also show iRF-LOOP's ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.

Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

Here we present 1. a model for amplicon sequencing 2. a definition of the best assignment of a read to a set of reference sequences 3. strategies and structures for indexing reference sequences and computing the best assign- ments of a set of reads efficiently, based on (ultra)metric spaces and their geometry The models, techniques, and ideas are particularly relevant to scenarios akin to 16S taxonomic profiling, where both the number of reference sequences and the read diversity is considerable.

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.

Clinically, knowing the number of red blood cells (RBCs) and white blood cells (WBCs) helps doctors to make the better decision on accurate diagnosis of numerous diseases. The manual cell counting is a very time-consuming and expensive process, and it depends on the experience of specialists. Therefore, a completely automatic method supporting cell counting is a viable solution for clinical laboratories. This paper proposes a novel blood cell counting procedure to address this challenge. The proposed method adopts SegNet - a deep learning semantic segmentation to simultaneously segment RBCs and WBCs. The global accuracy of the segmentation of WBCs, RBCs, and the background of peripheral blood smear images obtains 89% when segment WBCs and RBCs from the background of blood smear images. Moreover, an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform, local maxima, and connected component labeling. The counting result of the proposed procedure achieves an accuracy of 93.3% for red blood cell count using dataset 1 and 97.38% for white blood cell count using dataset 2.

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.

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

Data generated by metagenomic and metatranscriptomic experiments is both enormous and inherently noisy [1]. When using taxonomy-dependent alignment-based methods to classify and label reads, such as MEGAN [2], the first step consists in performing homology searches against sequence databases. To obtain the most information from the samples, nucleotide sequences are usually compared to various databases (i.e., nucleotide and protein) using local sequence aligners such as BLASTN and BLASTX [3]. Nevertheless, the analysis and integration of these results can be problematic because the outputs from these searches usually show differences, which can be notorious when working with RNA-seq (Personal observation; Graphical abstract). These inconsistencies led us to develop the HoSeIn workflow to determine the unequivocal taxonomic and functional profile of environmental samples, based on the assumption that the sequences that correspond to a certain taxon are composed of (Graphical abstract): 1) sequences that were assigned to the same taxon by both homology searches, plus 2) sequences that were assigned to that taxon by one of the homology searches but returned no hits in the other one, and vice versa.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major persisting problem is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. This entails investment in time, energy and expenses at various levels of research. To address these issues, a search engine design was recently been developed, which showed promise by revealing existing confirmatory published wet lab results. Additionally and of import, the engine mined up a range of unexplored/untested/unknown combinations of genetic factors in the Wnt pathway that were affected by ETC-1922159 enantiomer, a PORCN-WNT inhibitor, after the colorectal cancer cells were treated with the inhibitor drug. As an example, MYC is known to upregulate PRC2 complex. PRC2 complex contains EZH2, which suppresses tumor suppressor genes via epigenetic modifications. MYC and HOXB8 are up regulated in colorectal cancer, however, the dual working mechanism of the same is not known. The in silico engine showed positioning which correctly approximates and assigns to this 3rd order combination of MYC-HOXB8-EZH2, pointing to the in vitro/in vivo down regulation by ETC-1922159. If the protein interaction of MYC-HOXB8 can be established and a study be done apropos EZH2, it will establish at in vitro/in vivo level, the in silico ranking also. The potential of this engine is immense given the problem faced in biology and other fields. Here we elucidate the R code to understand the mechanics of the search engine in a fluid manner for systems biologists. Though the search engine is in the developmental stage, we share the detailed mechanism of the working principles of the same as it can be generalized to problems in other fields.

Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detail layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared to state of the art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

Data generation using high throughput technologies has led to the accumulation of diverse types of molecular data.These data have different types (discrete,real,string etc.) and occur in various formats and sizes. Datasets including gene expression, miRNA expression, protein-DNA binding data (ChIP-Seq/ChIP-ChIP), mutation data(copy number variation, single nucleotide polymorphisms), GO annotations, protein-protein interaction and disease-gene association data are some of the commonly used genomic datasets to study biological processes. Each of them provides a unique, complementary and partly independent view of the genome and hence embed essential information about their regulatory mechanisms. In order to understand the functions of genes, proteins and analyze mechanisms arising out of their interactions, information provided by each of these datasets individually may not be sufficient. Therefore integrating these multi-omic data and inferring regulatory interactions from the integrated dataset provides a system level biological insights in predicting gene functions and their phenotypic outcomes. To study genome functionality through interaction networks, different methods have been proposed for collective mining of information from an integrated dataset. We survey here data integration approaches using state-of-the-art techniques such as network integration, Bayesian networks, regularized regression (LASSO) and multiple kernel learning methods.

Parmigiano Reggiano cheese is one of the most appreciated and consumed food worldwide, especially in Italy, for its high content of nutrients and for its taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed in order to investigate the potentiality of this tool to distinguish rind percentage in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples in terms of percentage, seasoning and rind working process were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them with sensors responses. Data analysis consisted of two stages: multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results are promising in terms of correct classification of the samples. The classification rate is higher for ANNs than PLS-DA, reaching also 100% values.