Cited by 17Gene expression and cancer classificationPancreatic function and diabetesDiabetes and associated disorders

Detecting key genes relative expression orderings as biomarkers for machine learning-based intelligent screening and analysis of type 2 diabetes mellitus

Xie, Xueqin, Wu, Changchun, Ma, Caiyi, Gao, Dong, Su, Wei, Huang, Jian, Deng, Kejun, Yan, Dan +1 more·2024·Expert Systems with Applications

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Crossref · 10.1016/j.eswa.2024.124702

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65 of 65 references verified0 citation errors · 0 not checkable

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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.”

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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
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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