Peyton Greenside
Peyton Greenside
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TitleCited byYear
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
Learning important features through propagating activation differences
A Shrikumar, P Greenside, A Kundaje
Proceedings of the 34th International Conference on Machine Learning-Volume …, 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, 2016
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
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, 2017
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, 2017
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
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, 2017
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
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
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
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, 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
Reverse-complement parameter sharing improves deep learning models for genomics
A Shrikumar, P Greenside, A Kundaje
bioRxiv, 103663, 2017
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
Identification of a novel interspecific hybrid yeast from a metagenomic spontaneously inoculated beer sample using Hi‐C
C Smukowski Heil, JN Burton, I Liachko, A Friedrich, NA Hanson, ...
Yeast 35 (1), 71-84, 2018
Not just a black box: Interpretable deep learning by propagating activation differences
A Shrikumar, P Greenside, A Shcherbina, A Kundaje
ICML, 2016
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
Prediction of protein-ligand interactions from paired protein sequence motifs and ligand substructures.
P Greenside, M Hillenmeyer, A Kundaje
PSB, 20-31, 2018
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
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