ArticleFrontiers in cardiovascular medicine2022
Using machine learning to find genes associated with sudden death.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 3 citations in OpenAlex.
- IL18 Works Like a Two-Side Coin in Acute Pancreatitis.International journal of general medicine · 2026Article
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- Identification of a rare variant in TNNT3 responsible for familial dilated cardiomyopathy through whole-exome sequencing and in silico analysis.European journal of medical research · 2025Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To search for significant biomarkers associated with sudden death (SD). Methods: Differential genes were screened by comparing the whole blood samples from 15 cases of accidental death (AD) and 88 cases of SD. The protein-protein interaction (PPI) network selects core genes that interact most frequently. Machine learning is applied to find characteristic genes related to SD. The CIBERSORT method was used to explore the immune-microenvironment changes. Results: A total of 10 core genes (MYL1, TNNC2, TNNT3, TCAP, TNNC1, TPM2, MYL2, TNNI1, ACTA1, CKM) were obtained and they were mainly related to myocarditis, hypertrophic myocarditis and dilated cardiomyopathy (DCM). Characteristic genes of MYL2 and TNNT3 associated with SD were established by machine learning. There was no significant change in the immune-microenvironment before and after SD. Conclusion: Detecting characteristic genes is helpful to identify patients at high risk of SD and speculate the cause of death.
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