| 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 | 454 | 2019 |
| pomegranate: Fast and Flexible Probabilistic Modeling in Python J Schreiber Journal of Machine Learning Research 18 (164), 1-6, 2018 | 204 | 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 | 192 | 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 | 154 | 2019 |
| 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 | 125 | 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 | 121 | 2014 |
| 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 | 118 | 2022 |
| 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 | 101* | 2020 |
| 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 | 65 | 2020 |
| Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture J Schreiber, M Libbrecht, J Bilmes, WS Noble BioRxiv, 103614, 2017 | 59 | 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), 1-6, 2020 | 42 | 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 | 41 | 2023 |
| Analysis of Nanopore Data using Hidden Markov Models J Schreiber, K Karplus Bioinformatics, 2015 | 39 | 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), 1-13, 2020 | 37 | 2020 |
| apricot: Submodular selection for data summarization in Python J Schreiber, J Bilmes, WS Noble The Journal of Machine Learning Research 21 (1), 6474-6479, 2020 | 28 | 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 | 15* | 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 | 14 | 2022 |
| Ledidi: Designing genome edits that induce functional activity J Schreiber, YY Lu, WS Noble Proceedings of the ICML Workshop on Computational Biology, 2020 | 12 | 2020 |
| Machine learning for profile prediction in genomics J Schreiber, R Singh Current Opinion in Chemical Biology 65, 35-41, 2021 | 10 | 2021 |
| Finding the optimal Bayesian network given a constraint graph J Schreiber, W Noble PeerJ Computer Science, 2017 | 8 | 2017 |
William Stafford NobleProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
