Jacob Schreiber
Cited by
Cited by
A genome-wide framework for mapping gene regulation via cellular genetic screens
M Gasperini, AJ Hill, JL McFaline-Figueroa, B Martin, S Kim, MD Zhang, ...
Cell 176 (1), 377-390. e19, 2019
pomegranate: Fast and Flexible Probabilistic Modeling in Python
J Schreiber
Journal of Machine Learning Research 18 (164), 1-6, 2018
Error rates for nanopore discrimination among cytosine, methylcytosine, and hydroxymethylcytosine along individual DNA strands
J Schreiber, ZL Wescoe, R Abu-Shumays, JT Vivian, B Baatar, K Karplus, ...
Proceedings of the National Academy of Sciences 110 (47), 18910-18915, 2013
Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair
W Chen, A McKenna, J Schreiber, M Haeussler, Y Yin, V Agarwal, ...
Nucleic acids research 47 (15), 7989-8003, 2019
Navigating the pitfalls of applying machine learning in genomics
S Whalen, J Schreiber, WS Noble, KS Pollard
Nature Reviews Genetics 23 (3), 169-181, 2022
Nanopores discriminate among five C5-cytosine variants in DNA
ZL Wescoe, J Schreiber, M Akeson
Journal of the American Chemical Society 136 (47), 16582-16587, 2014
Discrimination among protein variants using an unfoldase-coupled nanopore
J Nivala, L Mulroney, G Li, J Schreiber, M Akeson
ACS nano 8 (12), 12365-12375, 2014
Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome
J Schreiber, T Durham, J Bilmes, WS Noble
Genome Biology 21 (1), 1-18, 2020
GENCODE: reference annotation for the human and mouse genomes in 2023
A Frankish, S Carbonell-Sala, M Diekhans, I Jungreis, JE Loveland, ...
Nucleic acids research 51 (D1), D942-D949, 2023
A high-throughput screen for transcription activation domains reveals their sequence features and permits prediction by deep learning
A Erijman, L Kozlowski, S Sohrabi-Jahromi, J Fishburn, L Warfield, ...
Molecular cell 78 (5), 890-902. e6, 2020
Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture
J Schreiber, M Libbrecht, J Bilmes, WS Noble
BioRxiv, 103614, 2017
A pitfall for machine learning methods aiming to predict across cell types
J Schreiber, R Singh, J Bilmes, WS Noble
Genome biology 21, 1-6, 2020
Analysis of Nanopore Data using Hidden Markov Models
J Schreiber, K Karplus
Bioinformatics, 2015
Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples
J Schreiber, J Bilmes, WS Noble
Genome biology 21, 1-13, 2020
apricot: Submodular selection for data summarization in Python
J Schreiber, J Bilmes, WS Noble
Journal of Machine Learning Research 21 (161), 1-6, 2020
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
J Rozowsky, J Gao, B Borsari, YT Yang, T Galeev, G Gürsoy, CB Epstein, ...
Cell 186 (7), 1493-1511. e40, 2023
fastISM: performant in silico saturation mutagenesis for convolutional neural networks
S Nair, A Shrikumar, J Schreiber, A Kundaje
Bioinformatics 38 (9), 2397-2403, 2022
Ledidi: Designing genome edits that induce functional activity
J Schreiber, YY Lu, WS Noble
Proceedings of the ICML Workshop on Computational Biology, 2020
Machine learning for profile prediction in genomics
J Schreiber, R Singh
Current Opinion in Chemical Biology 65, 35-41, 2021
Finding the optimal Bayesian network given a constraint graph
J Schreiber, W Noble
PeerJ Computer Science, 2017
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