Using unsupervised learning algorithms to identify essential genes associated with SARS-CoV-2 as potential therapeutic targets for COVID-19
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Abstract
Motivation
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures.
Results
We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19.
Availability
Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis .
Contact
m_habibi@qiau.ac.ir
Supplementary information
Supplementary data are available at Bioinformatics online.
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SciScore for 10.1101/2022.05.18.492443: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources This dataset contains the physical interactions between proteins that are collected from the Biological General Repository for Interaction Datasets (BioGRID) Chatr-Aryamontri et al (2017), Agile Protein Interactomes Data analyzer (APID) Alonso-Lopez et al (2019), Homologous interactions (Hint) Patil et al (2005), Human Integrated Protein Protein Interaction reference (HIPPIE) Alanis-Lobato. et al (2016) and Huri Luck et al (2020). BioGRIDsuggested: (BioGrid Australia, RRID:SCR_006334)Human Integrated Protein Proteinsuggested: NoneResults from OddPub: Thank you for sharing your code.
Results …SciScore for 10.1101/2022.05.18.492443: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources This dataset contains the physical interactions between proteins that are collected from the Biological General Repository for Interaction Datasets (BioGRID) Chatr-Aryamontri et al (2017), Agile Protein Interactomes Data analyzer (APID) Alonso-Lopez et al (2019), Homologous interactions (Hint) Patil et al (2005), Human Integrated Protein Protein Interaction reference (HIPPIE) Alanis-Lobato. et al (2016) and Huri Luck et al (2020). BioGRIDsuggested: (BioGrid Australia, RRID:SCR_006334)Human Integrated Protein Proteinsuggested: NoneResults from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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