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Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery
Concetta Schiano1, Monica Franzese2, Filippo Geraci3
1Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138 Naples, Italy.
Genes
|December 24, 2021
Summary
Machine learning identified novel genes for dilated cardiomyopathy (DCM). NEAT1 gene under-expression was significantly linked to left ventricular end-diastolic diameter in DCM patients.
Area of Science:
- Genomics
- Cardiovascular Research
- Bioinformatics
Background:
- Dilated cardiomyopathy (DCM) exhibits a distinct transcriptome, but its molecular network remains largely uncharacterized.
- Understanding the molecular underpinnings of DCM is crucial for identifying novel therapeutic targets.
Purpose of the Study:
- To identify specific disease-related molecular targets for DCM.
- To combine a novel machine learning (ML) approach with protein-protein interaction networks for target discovery.
Main Methods:
- Investigated transcriptomic profiles of human myocardial tissues using a Custom Decision Tree algorithm within a differential expression bioinformatic framework.
- Validated findings using quantitative real-time PCR.
Main Results:
- Discovered DCM-related genes including MYH6, NPPA, MT-RNR1, and NEAT1.
- Found a significant association between NEAT1 expression and left ventricular end-diastolic diameter (LVEDD) in NYHA-class III patients (Rho = 0.73, p = 0.05).
Conclusions:
- The ML approach facilitated the discovery of preliminary genes for rapid selection of DCM-correlated molecular targets.
- Demonstrated for the first time a significant association between NEAT1 under-expression and LVEDD in the human heart.
