A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

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@article{dream2017,
title = {A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines},
journal = {Cell Systems},
volume = {5},
number = {5},
pages = {485--497.e3},
year = {2017},
issn = {2405-4712},
doi = {10.1016/j.cels.2017.09.004},
url = {http://www.sciencedirect.com/science/article/pii/S2405471217303927},
author = {Mehmet G{\"o}nen and Barbara A. Weir and Glenn S. Cowley and Francisca Vazquez and Yuanfang Guan and Alok Jaiswal and Masayuki Karasuyama and Vladislav Uzunangelov and Tao Wang and Aviad Tsherniak and Sara Howell and Daniel Marbach and Bruce Hoff and Thea C. Norman and Antti Airola and Adrian Bivol and Kerstin Bunte and Daniel Carlin and Sahil Chopra and Alden Deran and Kyle Ellrott and Peddinti Gopalacharyulu and Kiley Graim and Samuel Kaski and Suleiman A. Khan and Yulia Newton and Sam Ng and Tapio Pahikkala and Evan Paull and Artem Sokolov and Hao Tang and Jing Tang and Krister Wennerberg and Yang Xie and Xiaowei Zhan and Fan Zhu and Tero Aittokallio and Hiroshi Mamitsuka and Joshua M. Stuart and Jesse S. Boehm and David E. Root and Guanghua Xiao and Gustavo Stolovitzky and William C. Hahn and Adam A. Margolin},
keywords = {cancer genomics, community challenge, crowdsourcing, functional screen, machine learning, oncogene},
abstract = {Summary We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.},
annotetest = {testannote},
}