Sungjoon Park
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ...
Nature communications 10 (1), 1-17, 2019
Assessment of network module identification across complex diseases
S Choobdar, ME Ahsen, J Crawford, M Tomasoni, T Fang, D Lamparter, ...
Nature methods 16 (9), 843-852, 2019
In silico drug combination discovery for personalized cancer therapy
M Jeon, S Kim, S Park, H Lee, J Kang
BMC systems biology 12 (2), 59-67, 2018
Enhancing the interpretability of transcription factor binding site prediction using attention mechanism
S Park, Y Koh, H Jeon, H Kim, Y Yeo, J Kang
Scientific Reports 10 (1), 1-10, 2020
Crowdsourced mapping of unexplored target space of kinase inhibitors
A Cichońska, B Ravikumar, RJ Allaway, F Wan, S Park, O Isayev, S Li, ...
Nature communications 12 (1), 1-18, 2021
Deep learning of mutation-gene-drug relations from the literature
K Lee, B Kim, Y Choi, S Kim, W Shin, S Lee, S Park, S Kim, AC Tan, ...
BMC bioinformatics 19 (1), 1-13, 2018
A community challenge for a pancancer drug mechanism of action inference from perturbational profile data
EF Douglass Jr, RJ Allaway, B Szalai, W Wang, T Tian, ...
Cell Reports Medicine 3 (1), 100492, 2022
BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations
K Lee, S Lee, S Park, S Kim, S Kim, K Choi, AC Tan, J Kang
Database 2016, 2016
BTNET: boosted tree based gene regulatory network inference algorithm using time-course measurement data
S Park, JM Kim, W Shin, SW Han, M Jeon, HJ Jang, IS Jang, J Kang
BMC systems biology 12 (2), 69-77, 2018
Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding
G Jang, S Park, S Lee, S Kim, S Park, J Kang
Bioinformatics 37 (Supplement_1), i376-i382, 2021
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
H Kim, M Sung, W Yoon, S Park, J Kang
arXiv preprint arXiv:2111.10584, 2021
Cancer mutations converge on a collection of protein assemblies to predict resistance to replication stress
X Zhao, A Singhal, S Park, JH Kong, R Bachelder, T Ideker
Cancer Discovery 14 (3), 508-523, 2024
CONFIGURE: A pipeline for identifying context specific regulatory modules from gene expression data and its application to breast cancer
S Park, D Hwang, YS Yeo, H Kim, J Kang
BMC medical genomics 12 (5), 1-8, 2019
A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors
S Park, E Silva, A Singhal, MR Kelly, K Licon, I Panagiotou, C Fogg, ...
Nature Cancer, 1-14, 2024
Cancer mutations converge on a constellation of molecular assemblies to predict resistance to replication stress agents
X Zhao, A Singhal, SJ Park, JH Kong, R Bachelder, T Ideker
Cancer Research 84 (6_Supplement), 4918-4918, 2024
G2PT: Mechanistic genotype-phenotype translation using hierarchical transformers
I Lee, S Park, H Nam, T Ideker
Cancer Research 84 (6_Supplement), 7383-7383, 2024
Full-text chemical identification with improved generalizability and tagging consistency
H Kim, M Sung, W Yoon, S Park, J Kang
Database 2022, 2022
Prediction of therapeutic response via data-driven maps of tumor cell architecture
EN Silva, A Singhal, S Park, J Kreisberg, T Ideker
Cancer Research 82 (12_Supplement), 636-636, 2022
Predicting clinical drug responses using a few-shot learning-based interpretable AI
S Park, A Singhal, E Silva, JF Kreisberg, T Ideker
Cancer Research 82 (12_Supplement), 1159-1159, 2022
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