MLBioIGE: integration and interplay of machine learning and bioinformatics approach to identify the genetic effect of SARS-COV-2 on idiopathic pulmonary fibrosis patients

Sk Tanzir Mehedi, Kawsar Ahmed, Francis M. Bui, Musfikur Rahaman, Imran Hossain, Tareq Mahmud Tonmoy, Rakibul Alam Limon, Sobhy M. Ibrahim, Mohammad Ali Moni

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
13 Downloads (Pure)

Abstract

SARS-CoV-2, the virus that causes COVID-19, is a current concern for people worldwide. The virus has recently spread worldwide and is out of control in several countries, putting the outbreak into a terrifying phase. Machine learning with transcriptome analysis has advanced in recent years. Its outstanding performance in several fields has emerged as a potential option to find out how SARS-CoV-2 is related to other diseases. Idiopathic pulmonary fibrosis (IPF) disease is caused by long-term lung injury, a risk factor for SARS-CoV-2. In this article, we used a variety of combinatorial statistical approaches, machine learning, and bioinformatics tools to investigate how the SARS-CoV-2 affects IPF patients' complexity. For this study, we employed two RNA-seq datasets. The unique contributions include common genes identification to identify shared pathways and drug targets, PPI network to identify hub-genes and basic modules, and the interaction of transcription factors (TFs) genes and TFs-miRNAs with common differentially expressed genes also placed on the datasets. Furthermore, we used gene ontology and molecular pathway analysis to do functional analysis and discovered that IPF patients have certain standard connections with the SARS-CoV-2 virus. A detailed investigation was carried out to recommend therapeutic compounds for IPF patients affected by the SARS-CoV-2 virus.
Original languageEnglish
Article numberbpac013
Pages (from-to)1-17
Number of pages17
JournalBiology Methods and Protocols
Volume7
Issue number1
DOIs
Publication statusPublished - May 2022

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