ArticleBiology methods & protocols2022
MLBioIGE: integration and interplay of machine learning and bioinformatics approach to identify the genetic effect of SARS-COV-2 on idiopathic pulmonary fibrosis patients.
Article in Biology methods & protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.Scientific reports · 2025Article
- Prevalence of covid-19 among patients with chronic obstructive pulmonary disease and tuberculosis.Annals of medicine · 2023Article
- Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.BioMed research international · 2023Article
- Integrated bioinformatic analysis reveals immune molecular markers and potential drugs for diabetic cardiomyopathy.Frontiers in endocrinology · 2022Article
- Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.