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Xin Ma

Researcher at Stanford University

Publications -  13
Citations -  2516

Xin Ma is an academic researcher from Stanford University. The author has contributed to research in topics: Genome & Population. The author has an hindex of 9, co-authored 11 publications receiving 2099 citations. Previous affiliations of Xin Ma include Cornell University.

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Mapping copy number variation by population-scale genome sequencing

Ryan E. Mills, +374 more
- 03 Feb 2011 - 
TL;DR: A map of unbalanced SVs is constructed based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations, and serves as a resource for sequencing-based association studies.

A map of human genome variation from population-scale sequencing

Richard Durbin, +361 more
TL;DR: The pilot phase of the 1000 Genomes Project is presented, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms, and the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants are described.
Journal ArticleDOI

Comparative and demographic analysis of orang-utan genomes.

Devin P. Locke, +106 more
- 27 Jan 2011 - 
TL;DR: The orang-utan species, Pongo abelii and Pongo pygmaeus, are the most phylogenetically distant great apes from humans, thereby providing an informative perspective on hominid evolution and a primate polymorphic neocentromere, found in both Pongo species are described.
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The Baker's Yeast Diploid Genome Is Remarkably Stable in Vegetative Growth and Meiosis

TL;DR: The results indicate that the diploid yeast nuclear genome is remarkably stable during the vegetative and meiotic cell cycles and support the hypothesis that peripheral regions of chromosomes are more dynamic than gene-rich central sections where structural rearrangements could be deleterious.
Journal ArticleDOI

Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis

TL;DR: The Xrare model is learned from a large database of clinical variants, and derives its strength from the tight integration of medical genetics features and phenotypic features similarity scores.