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Full-Text Articles in Life Sciences
Detecting Gene-Gene Interactions Using A Permutation-Based Random Forest Method, Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore
Detecting Gene-Gene Interactions Using A Permutation-Based Random Forest Method, Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore
Dartmouth Scholarship
Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.
Fastpop: A Rapid Principal Component Derived Method To Infer Intercontinental Ancestry Using Genetic Data, Yafang Li, Jinyoung Byun, Guoshuai Cai, Xiangjun Xiao, Younghun Han, Olivier Cornelis, James E. Dinulos, Joe Dennis, Douglas Easton, Ivan Gorlov, Michael F. Seldin, Christopher I. Amos
Fastpop: A Rapid Principal Component Derived Method To Infer Intercontinental Ancestry Using Genetic Data, Yafang Li, Jinyoung Byun, Guoshuai Cai, Xiangjun Xiao, Younghun Han, Olivier Cornelis, James E. Dinulos, Joe Dennis, Douglas Easton, Ivan Gorlov, Michael F. Seldin, Christopher I. Amos
Dartmouth Scholarship
Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the …