Detecting key genes relative expression orderings as biomarkers for machine learning-based intelligent screening and analysis of type 2 diabetes mellitus
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Traditional ML algorithms have been the cornerstone of early AI-assisted diagnostic models, particularly demonstrating robust performance in scenarios where features are well defined and sample sizes are moderate.
“Traditional ML algorithms have been the cornerstone of early AI-assisted diagnostic models [106–110], particularly demonstrating robust performance in scenarios where features are well defined and sample sizes are moderate.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [109]
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- Authors
- Xie, X., Wu, C., Ma, C., Gao, D., Su, W., Huang, J., Deng, K., Yan, D., & Lin, H.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Capital Medical University (CN)
- Corresponding author
- Deng, Kejun
- Yan, Dan
- Lin, Hao
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 255
- Pages
- 124702
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4400428885
- Cited by
- 17
- References
- 66
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 5
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Keywords
- type 2 diabetes, pancreatic islets, biomarkers, gene expression signatures, minimum redundancy maximum relevance, incremental feature selection, single-cell RNA sequencing, differential gene expression, disease screening, therapeutic target
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