|A community effort to assess and improve drug sensitivity prediction algorithms|
JC Costello, LM Heiser, E Georgii, M Gönen, MP Menden, NJ Wang, ...
Nature biotechnology 32 (12), 1202-1212, 2014
|Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open …|
The Lancet Oncology, 2016
|Prediction of human population responses to toxic compounds by a collaborative competition|
F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ...
Nature biotechnology 33 (9), 933-940, 2015
|Kernelized Bayesian matrix factorization|
M Gonen, SA Khan, S Kaski
Proceedings of The 30th International Conference on Machine Learning 28, 864-872, 2013
|Bayesian group factor analysis|
S Virtanen, A Klami, S Khan, S Kaski
Artificial Intelligence and Statistics, 1269-1277, 2012
|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
|Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis|
SK Sieberts, F Zhu, J García-García, E Stahl, A Pratap, G Pandey, ...
Nature communications 7 (1), 1-10, 2016
|Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling|
EI Andersson, S Pützer, B Yadav, O Dufva, S Khan, L He, L Sellner, ...
Leukemia 32 (3), 774-787, 2018
|Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization|
M Ammad-Ud-Din, SA Khan, D Malani, A Murumägi, O Kallioniemi, ...
Bioinformatics 32 (17), i455-i463, 2016
|Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis|
SA Khan, S Virtanen, OP Kallioniemi, K Wennerberg, A Poso, S Kaski
Bioinformatics 30 (17), i497-i504, 2014
|Drug target commons: a community effort to build a consensus knowledge base for drug-target interactions|
J Tang, B Ravikumar, Z Alam, A Rebane, M Vähä-Koskela, G Peddinti, ...
Cell chemical biology 25 (2), 224-229. e2, 2018
|Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression|
M Ammad-Ud-Din, SA Khan, K Wennerberg, T Aittokallio
Bioinformatics 33 (14), i359-i368, 2017
|Bayesian multi-view tensor factorization|
SA Khan, S Kaski
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014
|Bayesian multi-tensor factorization|
SA Khan, E Leppäaho, S Kaski
Machine Learning 105 (2), 233-253, 2016
|Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach|
M Ali, SA Khan, K Wennerberg, T Aittokallio
Bioinformatics 34 (8), 1353-1362, 2018
|Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs|
SA Khan, A Faisal, JP Mpindi, JA Parkkinen, T Kalliokoski, A Poso, ...
BMC bioinformatics 13 (1), 112, 2012
|Convex factorization machine for toxicogenomics prediction|
M Yamada, W Lian, A Goyal, J Chen, K Wimalawarne, SA Khan, S Kaski, ...
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017
|Scalable similarity matching in streaming time series|
A Marascu, SA Khan, T Palpanas
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 218-230, 2012
|Transcriptional response networks for elucidating mechanisms of action of multitargeted agents|
M Kibble, SA Khan, N Saarinen, F Iorio, J Saez-Rodriguez, S Mäkelä, ...
Drug discovery today 21 (7), 1063-1075, 2016
|A community challenge for inferring genetic predictors of gene essentialities through analysis of a functional screen of cancer cell lines|
M Gönen, BA Weir, GS Cowley, F Vazquez, Y Guan, A Jaiswal, ...
Cell systems 5 (5), 485-497. e3, 2017