An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.

Journal: Artificial intelligence in medicine

PMID: 31980105

Abstract

OBJECTIVE: Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research.

Authors

Cheng-Hong Yang

Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. chyang@cc.kuas.edu.tw.
Li-Yeh Chuang

Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan. chuang@isu.edu.tw.
Yu-Da Lin

Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. e0955767257@yahoo.com.tw.

Keywords

Algorithms Artificial Intelligence Case-Control Studies Drug Resistance Drug Resistance, Multiple Epistasis, Genetic Fuzzy Logic Genotype Humans Models, Genetic Multifactor Dimensionality Reduction

External Resources

View on PubMed Access via DOI PubMed (31980105)

An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.

Abstract

Authors

Keywords

External Resources

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