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Articles 1 - 3 of 3
Full-Text Articles in Computational Biology
Ten Simple Rules For Taking Advantage Of Git And Github, Yasset Perez-Riverol, Laurent Gatto, Rui Wang, Timo Sachsenberg, Julian Uszkoreit, Felipe Da Veiga Leprevost, Christian Fufezan, Tobias Ternent, Stephen J. Eglen, Daniel S. Katz, Tom J. Pollard, Alexander Konovalov, Robert M. Flight, Kai Blin, Juan Antonio Vizcaíno
Ten Simple Rules For Taking Advantage Of Git And Github, Yasset Perez-Riverol, Laurent Gatto, Rui Wang, Timo Sachsenberg, Julian Uszkoreit, Felipe Da Veiga Leprevost, Christian Fufezan, Tobias Ternent, Stephen J. Eglen, Daniel S. Katz, Tom J. Pollard, Alexander Konovalov, Robert M. Flight, Kai Blin, Juan Antonio Vizcaíno
Molecular and Cellular Biochemistry Faculty Publications
No abstract provided.
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 …
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
COBRA Preprint Series
Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …