miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression...

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Veröffentlicht in:PeerJ (San Francisco, CA) Jg. 9; S. e11458
Hauptverfasser: Yousef, Malik, Goy, Gokhan, Mitra, Ramkrishna, Eischen, Christine M., Jabeer, Amhar, Bakir-Gungor, Burcu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Diego PeerJ. Ltd 19.05.2021
PeerJ, Inc
PeerJ Inc
Schlagworte:
Algorithms
Analysis
Artificial Intelligence
Bioinformatics
Biomarkers
Cancer
Data analysis
Data Mining and Machine Learning
Gene expression
Genes
Genomes
Grouping
Health aspects
Integrated
Integration
Learning algorithms
Machine learning
Messenger RNA
Methods
MicroRNA
miRNA
Ranking
ISSN:2167-8359, 2167-8359
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Zusammenfassung:A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet .
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ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.11458