An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria

DOI10.3934/MBE.2021382zbMath1501.92043OpenAlexW3197994185MaRDI QIDQ2092177

Xiong Li, Haowen Chen, Xia Chen, Yexiong Lin, Qiang Qu, Bing Ning

Publication date: 2 November 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2021382

zbMATH Keywords

genetic algorithminformation entropyBayesian networkgenome variationtumor subtype classification

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Medical applications (general) (92C50)

Uses Software

• XGBoost

Cites Work

• Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC
• A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

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