Sara Mathieson
Sara Mathieson
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Estimating variable effective population sizes from multiple genomes: a sequentially Markov conditional sampling distribution approach
S Sheehan, K Harris, YS Song
Genetics 194 (3), 647-662, 2013
Deep learning for population genetic inference
S Sheehan, YS Song
PLoS computational biology 12 (3), e1004845, 2016
FADS1 and the Timing of Human Adaptation to Agriculture
S Mathieson, I Mathieson
Molecular biology and evolution 35 (12), 2957-2970, 2018
Telescoper: de novo assembly of highly repetitive regions
M Bresler, S Sheehan, AH Chan, YS Song
Bioinformatics 28 (18), i311-i317, 2012
A likelihood-free inference framework for population genetic data using exchangeable neural networks
J Chan, V Perrone, J Spence, P Jenkins, S Mathieson, Y Song
Advances in neural information processing systems, 8594-8605, 2018
Decoding coalescent hidden Markov models in linear time
K Harris, S Sheehan, JA Kamm, YS Song
International Conference on Research in Computational Molecular Biology, 100-114, 2014
ImaGene: a convolutional neural network to quantify natural selection from genomic data
L Torada, L Lorenzon, A Beddis, U Isildak, L Pattini, S Mathieson, ...
BMC bioinformatics 20 (9), 337, 2019
Automatic inference of demographic parameters using Generative Adversarial Networks
Z Wang, J Wang, M Kourakos, N Hoang, HH Lee, I Mathieson, ...
bioRxiv, 2020
Ancestral Haplotype Reconstruction in Endogamous Populations using Identity-By-Descent
K Finke, M Kourakos, G Brown, YB Simons, AA Schaffer, RL Kember, ...
bioRxiv, 2020
Distributed Pipeline for Genomic Variant Calling
R Xia, S Sheehan, Y Zhang, A Talwalkar, M Zaharia, J Terhorst, M Jordan, ...
NIPS Workshop on BIG Learning, 2012
CSC# 390# Topics# in# Artificial# Intelligence
S Mathieson
Deep Learning for Population Genetic Inference
S Mathieson, YS Song
Scalable Algorithms for Population Genomic Inference
S Sheehan
UC Berkeley, 2015
Estimating variable effective population sizes from multiple genomes: A sequentially Markov conditional sampling distribution approach
S Mathieson, K Harris, YS Song
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