C
Carlos Bustamante
Researcher at Stanford University
Publications - 799
Citations - 122303
Carlos Bustamante is an academic researcher from Stanford University. The author has contributed to research in topics: Population & DNA. The author has an hindex of 161, co-authored 770 publications receiving 106053 citations. Previous affiliations of Carlos Bustamante include Lawrence Berkeley National Laboratory & University of California.
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Journal ArticleDOI
Fast, exact linkage analysis for categorical traits on arbitrary pedigree designs
Abra Brisbin,Abra Brisbin,Jenifer Cruickshank,Jenifer Cruickshank,N. Sydney Moïse,Teresa M. Gunn,Carlos Bustamante,Carlos Bustamante,Jason G. Mezey +8 more
TL;DR: This paper presents LOCate v.2, a fast, exact algorithm for linkage analysis of all types of categorical traits, both ordinal and nominal, which is able to incorporate missing data and analyze complex genealogical structure, including inbreeding loops.
Journal ArticleDOI
Letting the cat out of the bag: a personal journey in Biophysics.
TL;DR: Today, these have become two of the most fertile research areas in Biophysics: a renewed push for a quantitative, precise description of biological systems at the molecular level, and efforts towards an integrated understanding of the operation, control, and coordination of cellular processes.
Posted ContentDOI
Inference of Gorilla demographic and selective history from whole genome sequence data
Kimberly F. McManus,Joanna L. Kelley,Shiya Song,Krishna R. Veeramah,August E. Woerner,Laurie S. Stevison,Oliver A. Ryder,Jeffrey M. Kidd,Jeffrey D. Wall,Carlos Bustamante,Michael Hammer +10 more
TL;DR: It is found that processes related to taste, pancreatic and saliva secretion, sodium ion transmembrane transport, and cardiac muscle function are overrepresented in genomic regions predicted to have experienced recent positive selection.
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Neural ADMIXTURE for rapid genomic clustering
TL;DR: Neural ADMIXTURE as mentioned in this paper is a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives.
Posted ContentDOI
Archetypal Analysis for Population Genetics
TL;DR: In this paper, the singular value decomposition (SVD) was combined with archetypal analysis to perform fast and accurate genetic clustering by first reducing the dimensionality of the space of genomic sequences.