| Learning important features through propagating activation differences A Shrikumar, P Greenside, A Kundaje International conference on machine learning, 3145-3153, 2017 | 4690 | 2017 |
| Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of the royal society interface 15 (141), 20170387, 2018 | 2126 | 2018 |
| Not just a black box: Learning important features through propagating activation differences A Shrikumar, P Greenside, A Shcherbina, A Kundaje arXiv preprint arXiv:1605.01713, 2016 | 823 | 2016 |
| Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage JA Wamstad, JM Alexander, RM Truty, A Shrikumar, F Li, KE Eilertson, ... Cell 151 (1), 206-220, 2012 | 668 | 2012 |
| Base-resolution models of transcription-factor binding reveal soft motif syntax Ž Avsec, M Weilert, A Shrikumar, S Krueger, A Alexandari, K Dalal, ... Nature genetics 53 (3), 354-366, 2021 | 438 | 2021 |
| Transcriptional reversion of cardiac myocyte fate during mammalian cardiac regeneration CC O’Meara, JA Wamstad, RA Gladstone, GM Fomovsky, VL Butty, ... Circulation research 116 (5), 804-815, 2015 | 174 | 2015 |
| The Kipoi repository accelerates community exchange and reuse of predictive models for genomics Ž Avsec, R Kreuzhuber, J Israeli, N Xu, J Cheng, A Shrikumar, A Banerjee, ... Nature biotechnology 37 (6), 592-600, 2019 | 158 | 2019 |
| Technical note on transcription factor motif discovery from importance scores (TF-MoDISco) version 0.5. 6.5 A Shrikumar, K Tian, Ž Avsec, A Shcherbina, A Banerjee, M Sharmin, ... arXiv preprint arXiv:1811.00416, 2018 | 143 | 2018 |
| Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation A Alexandari, A Kundaje, A Shrikumar International Conference on Machine Learning, 222-232, 2020 | 135 | 2020 |
| Proceedings of the 34th International Conference on Machine Learning A Shrikumar, P Greenside, A Kundaje, P Doina, WT Yee vol. 70 of Proceedings of Machine Learning Research, 3145-3153, 2017 | 109 | 2017 |
| Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays R Movva, P Greenside, GK Marinov, S Nair, A Shrikumar, A Kundaje PLoS One 14 (6), e0218073, 2019 | 93 | 2019 |
| Short tandem repeats bind transcription factors to tune eukaryotic gene expression CA Horton, AM Alexandari, MGB Hayes, E Marklund, JM Schaepe, ... Science 381 (6664), eadd1250, 2023 | 70 | 2023 |
| Reverse-complement parameter sharing improves deep learning models for genomics A Shrikumar, P Greenside, A Kundaje BioRxiv, 103663, 2017 | 63 | 2017 |
| Learning important features through propagating activation differences. arXiv A Shrikumar, P Greenside, A Kundaje arXiv preprint arXiv:1704.02685 10, 2017 | 53 | 2017 |
| GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs A Shrikumar, E Prakash, A Kundaje Bioinformatics 35 (14), i173-i182, 2019 | 49 | 2019 |
| Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics A Tseng, A Shrikumar, A Kundaje Advances in Neural Information Processing Systems 33, 1913-1923, 2020 | 33 | 2020 |
| Deep learning at base-resolution reveals motif syntax of the cis-regulatory code Z Avsec, M Weilert, A Shrikumar, A Alexandari, S Krueger, K Dalal, ... BioRxiv 737981, 2019 | 31 | 2019 |
| Domain-adaptive neural networks improve cross-species prediction of transcription factor binding K Cochran, D Srivastava, A Shrikumar, A Balsubramani, RC Hardison, ... Genome research 32 (3), 512-523, 2022 | 22 | 2022 |
| fastISM: performant in silico saturation mutagenesis for convolutional neural networks S Nair, A Shrikumar, J Schreiber, A Kundaje Bioinformatics 38 (9), 2397-2403, 2022 | 19 | 2022 |
| Computationally efficient measures of internal neuron importance A Shrikumar, J Su, A Kundaje arXiv preprint arXiv:1807.09946, 2018 | 19 | 2018 |
