Jacob Schreiber
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 (1-2), 377-390. e19, 2019
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
pomegranate: Fast and Flexible Probabilistic Modeling in Python
J Schreiber
Journal of Machine Learning Research 18 (164), 1-6, 2018
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
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
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
Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture
J Schreiber, M Libbrecht, J Bilmes, WS Noble
bioRxiv, 103614, 2018
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
Analysis of Nanopore Data using Hidden Markov Models
J Schreiber, K Karplus
Bioinformatics, 2015
A pitfall for machine learning methods aiming to predict across cell types
J Schreiber, R Singh, J Bilmes, WS Noble
Genome biology 21 (1), 1-6, 2020
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), 1-13, 2020
Navigating the pitfalls of applying machine learning in genomics
S Whalen, J Schreiber, WS Noble, KS Pollard
Nature Reviews Genetics, 1-13, 2021
apricot: Submodular selection for data summarization in Python.
JM Schreiber, JA Bilmes, WS Noble
J. Mach. Learn. Res. 21, 161:1-161:6, 2020
Ledidi: Designing genome edits that induce functional activity
J Schreiber, YY Lu, WS Noble
Proceedings of the ICML Workshop on Computational Biology, 2020
Finding the optimal Bayesian network given a constraint graph
J Schreiber, W Noble
PeerJ Computer Science, 2017
Segmentation of Noisy Signals Generated by a Nanopore
J Schreiber, K Karplus
bioRxiv, 2015
Machine learning for profile prediction in genomics
J Schreiber, R Singh
Current opinion in chemical biology 65, 35-41, 2021
Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data
J Schreiber, J Bilmes, WS Noble
Bioinformatics 37 (4), 439-447, 2021
Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics
J Schreiber, D Hegde, WS Noble
Proceedings of the 11th ACM International Conference on Bioinformatics …, 2020
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