Avanti Shrikumar
Avanti Shrikumar
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Learning important features through propagating activation differences
A Shrikumar, P Greenside, A Kundaje
International conference on machine learning, 3145-3153, 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
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
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
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
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
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
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
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
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
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
Reverse-complement parameter sharing improves deep learning models for genomics
A Shrikumar, P Greenside, A Kundaje
BioRxiv, 103663, 2017
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
Learning important features through propagating activation differences. arXiv
A Shrikumar, P Greenside, A Kundaje
arXiv preprint arXiv:1704.02685 10, 2017
GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs
A Shrikumar, E Prakash, A Kundaje
Bioinformatics 35 (14), i173-i182, 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
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
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
fastISM: performant in silico saturation mutagenesis for convolutional neural networks
S Nair, A Shrikumar, J Schreiber, A Kundaje
Bioinformatics 38 (9), 2397-2403, 2022
Towards more realistic simulated datasets for benchmarking deep learning models in regulatory genomics
EI Prakash, A Shrikumar, A Kundaje
Machine Learning in Computational Biology, 58-77, 2022
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