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Evan M Cofer

Showing results (1-10 of 8) with videos related to

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Nature Methods|March 30, 2019
Selene: a PyTorch-based deep learning library for sequence dataKathleen M Chen, Evan M Cofer, Jian Zhou, et al.
Bioinformatics (Oxford, England)|December 12, 2017
Aether: leveraging linear programming for optimal cloud computing in genomicsJacob M Luber, Braden T Tierney, Evan M Cofer, et al.
Cell Genomics|October 11, 2025
Saturating the eQTL map in Drosophila: Genome-wide patterns of cis and trans regulation of transcriptional variation in outbred populationsLuisa F Pallares, Diogo Melo, Scott Wolf, et al.
Plos Computational Biology|May 12, 2020
A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery typeAlan Le Goallec, Braden T Tierney, Jacob M Luber, et al.
Genome Research|April 23, 2021
Modeling transcriptional regulation of model species with deep learningEvan M Cofer, João Raimundo, Alicja Tadych, et al.
Cell Genomics|September 16, 2025
3D chromatin structures precede genome activation in Drosophila embryogenesisGabriel A Dolsten, Evan M Cofer, Xin Yang Bing, et al.
Plos Computational Biology|March 24, 2022
Ten quick tips for deep learning in biologyBenjamin D Lee, Anthony Gitter, Casey S Greene, et al.
Journal of the Royal Society, Interface|April 6, 2018
Opportunities and obstacles for deep learning in biology and medicineTravers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Nature Methods|March 30, 2019
Selene: a PyTorch-based deep learning library for sequence dataKathleen M Chen, Evan M Cofer, Jian Zhou, et al.
Bioinformatics (Oxford, England)|December 12, 2017
Aether: leveraging linear programming for optimal cloud computing in genomicsJacob M Luber, Braden T Tierney, Evan M Cofer, et al.
Cell Genomics|October 11, 2025
Saturating the eQTL map in Drosophila: Genome-wide patterns of cis and trans regulation of transcriptional variation in outbred populationsLuisa F Pallares, Diogo Melo, Scott Wolf, et al.
Plos Computational Biology|May 12, 2020
A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery typeAlan Le Goallec, Braden T Tierney, Jacob M Luber, et al.
Genome Research|April 23, 2021
Modeling transcriptional regulation of model species with deep learningEvan M Cofer, João Raimundo, Alicja Tadych, et al.
Cell Genomics|September 16, 2025
3D chromatin structures precede genome activation in Drosophila embryogenesisGabriel A Dolsten, Evan M Cofer, Xin Yang Bing, et al.
Plos Computational Biology|March 24, 2022
Ten quick tips for deep learning in biologyBenjamin D Lee, Anthony Gitter, Casey S Greene, et al.
Journal of the Royal Society, Interface|April 6, 2018
Opportunities and obstacles for deep learning in biology and medicineTravers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, et al.
Pageof 1