An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions | Zendy

David J. Miller | Zendy; Yanxin Zhang | Zendy; Guoqiang Yu | Zendy; Yongmei Liu | Zendy; Li Chen | Zendy; Carl D. Langefeld | Zendy; David M. Herrington | Zendy; Yue Wang | Zendy

Open Access

An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions

Author(s) -

David J. Miller,

Yanxin Zhang,

Guoqiang Yu,

Yongmei Liu,

Li Chen,

Carl D. Langefeld,

David M. Herrington,

Yue Wang

Publication year - 2009

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btp435

Subject(s) - computer science , genome wide association study , principle of maximum entropy , single nucleotide polymorphism , computational biology , support vector machine , bayes' theorem , bayesian probability , feature selection , machine learning , artificial intelligence , biology , genetics , genotype , gene

In both genome-wide association studies (GWAS) and pathway analysis, the modest sample size relative to the number of genetic markers presents formidable computational, statistical and methodological challenges for accurately identifying markers/interactions and for building phenotype-predictive models.

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