Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sara Sheehan"'
Autor:
Sara Sheehan, Yun S Song
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 3, p e1004845 (2016)
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning,
Externí odkaz:
https://doaj.org/article/2ec9cf38b44f429a8f572837592c1f75
Publikováno v:
Genetics. 194:647-662
Throughout history, the population size of modern humans has varied considerably due to changes in environment, culture, and technology. More accurate estimates of population size changes, and when they occurred, should provide a clearer picture of h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0baa9abf3ad56255876af9f518922334
https://doi.org/10.1534/genetics.112.149096
https://doi.org/10.1534/genetics.112.149096
Publikováno v:
Bioinformatics
Motivation: With advances in sequencing technology, it has become faster and cheaper to obtain short-read data from which to assemble genomes. Although there has been considerable progress in the field of genome assembly, producing high-quality de no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b097fa8dcbb8d2b2f5681432d8cdb74
https://doi.org/10.1093/bioinformatics/bts399
https://doi.org/10.1093/bioinformatics/bts399
Autor:
Yun S. Song, Sara Sheehan
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 3, p e1004845 (2016)
PLoS Computational Biology
PLoS Computational Biology
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::658dc574e0e2346b3b26302389f798e0
https://doi.org/10.1101/028175
https://doi.org/10.1101/028175
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319052687
RECOMB
RECOMB
In many areas of computational biology, hidden Markov models (HMMs) have been used to model local genomic features. In particular, coalescent HMMs have been used to infer ancient population sizes, migration rates, divergence times, and other paramete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd01009677148727069004e8cb48efce
http://arxiv.org/abs/1403.0858
http://arxiv.org/abs/1403.0858
Publikováno v:
Review of Radical Political Economics. 6:80-94
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d49d223aad537e4901e0c7bfebddeee
https://doi.org/10.1177/048661347500600409
https://doi.org/10.1177/048661347500600409
Autor:
Charles Press, Sara Sheehan
Publikováno v:
American Politics Quarterly. 1:126-137
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25d1dd690ce5eba54cf9881d9789492e
https://doi.org/10.1177/1532673x7300100106
https://doi.org/10.1177/1532673x7300100106