ArticleFrontiers in cardiovascular medicine2022
Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy.
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 2 papers.
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Who cites it
2 citing papers in PubMed, 8 citations in OpenAlex.
- Red blood cell distribution width to albumin ratio associates with prevalence and long-term diabetes mellitus prognosis: an overview of NHANES 1999-2020 data.Frontiers in endocrinology · 2024Article
- Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.Journal of translational medicine · 2023Article
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Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
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Abstract
Introduction: Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM. Methods: Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated). Results: We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 ( Discussion: SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.
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