Cancer classification and functional pathway discovery using TCGA transcriptomic profiles: A matched case-control framework

Leveraging high-dimensional transcriptomic data from The Cancer Genome Atlas (TCGA) for cancer classification holds critical significance for advancing precision oncology. Matched Case-control Design (MCCD), by pairing similar cases with controls, can enhance statistical power and reduce confounding...

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Vydané v:	Journal of bioinformatics and computational biology Ročník 23; číslo 5; s. 2550015
Hlavní autori:	Wang, Jie-Huei, Guo, Tzung-Ying, Pai, Yen-Yi, Hou, Po-Lin, Kumari, Himani, Chan, Michael W Y
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore 01.10.2025
Predmet:	Algorithms Case-Control Studies Computational Biology - methods Databases, Genetic Gene Expression Profiling - methods Humans Machine Learning Neoplasms - classification Neoplasms - genetics Neoplasms - metabolism Transcriptome matched case-control design model-based gene set analysis machine learning classification matched-pairs feature screening incremental feature selection TCGA Corrected feature matrix transformation
ISSN:	1757-6334, 1757-6334
On-line prístup:	Zistit podrobnosti o prístupe
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