Peyton Greenside
Peyton Greenside
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Cited by
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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
A next generation connectivity map: L1000 platform and the first 1,000,000 profiles
A Subramanian, R Narayan, SM Corsello, DD Peck, TE Natoli, X Lu, ...
Cell 171 (6), 1437-1452. e17, 2017
An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues
MR Corces, AE Trevino, EG Hamilton, PG Greenside, ...
Nature methods 14 (10), 959-962, 2017
Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution
MR Corces, JD Buenrostro, B Wu, PG Greenside, SM Chan, JL Koenig, ...
Nature genetics 48 (10), 1193-1203, 2016
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
Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements
MR Mumbach, AT Satpathy, EA Boyle, C Dai, BG Gowen, SW Cho, ...
Nature genetics 49 (11), 1602-1612, 2017
Discovery of common and rare genetic risk variants for colorectal cancer
JR Huyghe, SA Bien, TA Harrison, HM Kang, S Chen, SL Schmit, ...
Nature genetics 51 (1), 76-87, 2019
Genetic control of chromatin states in humans involves local and distal chromosomal interactions
F Grubert, JB Zaugg, M Kasowski, O Ursu, DV Spacek, AR Martin, ...
Cell 162 (5), 1051-1065, 2015
Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map
I Smith, PG Greenside, T Natoli, DL Lahr, D Wadden, I Tirosh, R Narayan, ...
PLoS biology 15 (11), e2003213, 2017
Molecular definition of a metastatic lung cancer state reveals a targetable CD109–Janus kinase–Stat axis
CH Chuang, PG Greenside, ZN Rogers, JJ Brady, D Yang, RK Ma, ...
Nature medicine 23 (3), 291-300, 2017
Landscape of cohesin-mediated chromatin loops in the human genome
F Grubert, R Srivas, DV Spacek, M Kasowski, M Ruiz-Velasco, ...
Nature 583 (7818), 737-743, 2020
Impact of regulatory variation across human iPSCs and differentiated cells
NE Banovich, YI Li, A Raj, MC Ward, P Greenside, D Calderon, PY Tung, ...
Genome research 28 (1), 122-131, 2018
Intertumoral heterogeneity in SCLC is influenced by the cell type of origin
D Yang, SK Denny, PG Greenside, AC Chaikovsky, JJ Brady, Y Ouadah, ...
Cancer discovery 8 (10), 1316-1331, 2018
Mitigation of off-target toxicity in CRISPR-Cas9 screens for essential non-coding elements
J Tycko, M Wainberg, GK Marinov, O Ursu, GT Hess, BK Ego, Aradhana, ...
Nature communications 10 (1), 4063, 2019
An Arntl2-driven secretome enables lung adenocarcinoma metastatic self-sufficiency
JJ Brady, CH Chuang, PG Greenside, ZN Rogers, CW Murray, ...
Cancer cell 29 (5), 697-710, 2016
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
Discovering epistatic feature interactions from neural network models of regulatory DNA sequences
P Greenside, T Shimko, P Fordyce, A Kundaje
Bioinformatics 34 (17), i629-i637, 2018
Relating chemical structure to cellular response: an integrative analysis of gene expression, bioactivity, and structural data across 11,000 compounds
B Chen, P Greenside, H Paik, M Sirota, D Hadley, AJ Butte
CPT: pharmacometrics & systems pharmacology 4 (10), 576-584, 2015
Accelerating bayesian optimization for biological sequence design with denoising autoencoders
S Stanton, W Maddox, N Gruver, P Maffettone, E Delaney, P Greenside, ...
International Conference on Machine Learning, 20459-20478, 2022
Reverse-complement parameter sharing improves deep learning models for genomics
A Shrikumar, P Greenside, A Kundaje
BioRxiv, 103663, 2017
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