REVIEW | doi:10.20944/preprints202203.0398.v1
Subject: Medicine & Pharmacology, Pediatrics Keywords: bibliometric analysis; Pubmed; rubella virus; research output; research collaboration; Biblioshiny; Bibliometrix; R-package
Online: 31 March 2022 (07:37:09 CEST)
Background: This work aimed to undertake a bibliometric analysis of the Rubella virus. Medical studies were conducted between 2000 and 2022 to discover trends, dynamics, and research outputs in the industry. Methods:A bibliometric study was performed using R software to determine research characteristics indexed worldwide and published in Rubella research in medical studies. The Rubella virus was chosen as the subject in the PUBMED database, and 374 papers from the previous two decades were reviewed. Results: There was an increase in the number of publications after 2003. The United States was the most essential countryamong all which had the most contributions on Rubella Virus. Conclusion:Rubella research has increased in the medical profession over the previous decade, with the United States leading to publications in this field.
REVIEW | doi:10.20944/preprints202105.0056.v2
Subject: Medicine & Pharmacology, General Medical Research Keywords: artificial intelligence; machine learning; deep learning; neural networks; biomedicine; healthcare; medicine; literature; PubMed; Embase
Online: 9 June 2021 (11:23:48 CEST)
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.
ARTICLE | doi:10.20944/preprints202208.0305.v1
Subject: Medicine & Pharmacology, Allergology Keywords: drug repurposing; combination therapeutics; PubMed; ChEBI; disease ontology; gene ontology; drug interaction; MeSH terms; COVID-19
Online: 17 August 2022 (05:51:53 CEST)
This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.
ARTICLE | doi:10.20944/preprints202106.0482.v3
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: COVID-19 Infodemic; Text Classification; TFIDF Features; Network Training modes; Supervised Learning; Misinformation; News Classification; False Publications; PubMed; Anomaly Detection
Online: 26 July 2021 (12:06:04 CEST)
The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.