Yakir A Reshef
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Explore the profile of Yakir A Reshef including associated specialties, affiliations and a list of published articles.
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Recent Articles
1.
Reshef Y, Rumker L, Kang J, Nathan A, Korsunsky I, Asgari S, et al.
Nat Biotechnol
. 2021 Oct;
40(3):355-363.
PMID: 34675423
As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current...
2.
Abuelezam N, Reshef Y, Novak D, Grad Y, Seage Iii G, Mayer K, et al.
J Med Internet Res
. 2019 Sep;
21(9):e13766.
PMID: 31516124
Background: The structure of the sexual networks and partnership characteristics of young black men who have sex with men (MSM) may be contributing to their high risk of contracting HIV...
3.
Galinsky K, Reshef Y, Finucane H, Loh P, Zaitlen N, Patterson N, et al.
Genet Epidemiol
. 2018 Nov;
43(2):180-188.
PMID: 30474154
Recent studies have examined the genetic correlations of single-nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated...
4.
Reshef Y, Finucane H, Kelley D, Gusev A, Kotliar D, Ulirsch J, et al.
Nat Genet
. 2018 Sep;
50(10):1483-1493.
PMID: 30177862
Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal....
5.
Loh P, Genovese G, Handsaker R, Finucane H, Reshef Y, Palamara P, et al.
Nature
. 2018 Jul;
559(7714):350-355.
PMID: 29995854
The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered...
6.
Finucane H, Reshef Y, Anttila V, Slowikowski K, Gusev A, Byrnes A, et al.
Nat Genet
. 2018 Apr;
50(4):621-629.
PMID: 29632380
We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium...
7.
Kelley D, Reshef Y, Bileschi M, Belanger D, McLean C, Snoek J
Genome Res
. 2018 Mar;
28(5):739-750.
PMID: 29588361
Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell-type-specific epigenetic and transcriptional...
8.
Loh P, Danecek P, Palamara P, Fuchsberger C, Reshef Y, Finucane H, et al.
Nat Genet
. 2016 Oct;
48(11):1443-1448.
PMID: 27694958
Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing in a genotyped cohort, an approach that can yield high accuracy in...
9.
Reshef D, Reshef Y, Mitzenmacher M, Sabeti P
Proc Natl Acad Sci U S A
. 2014 Aug;
111(33):E3362-3.
PMID: 25139972
No abstract available.
10.
Reshef D, Reshef Y, Finucane H, Grossman S, McVean G, Turnbaugh P, et al.
Science
. 2011 Dec;
334(6062):1518-24.
PMID: 22174245
Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC...