@article{895b3042e9e74dde935f6c96039c3b78,
title = "Minimizing bias in target selection by exploiting multidisciplinary Big Data and the protein interactome",
keywords = "Big Data, cancer, cancer networks, drug combinations, drug discovery, drug resistance, knowledgebase, target selection",
author = "Bissan Al-Lazikani and Paul Workman",
note = "Funding Information: The authors work at The Institute of Cancer Research, London, UK, which has a commercial interest in the discovery and development of anticancer drugs for different cancer types and has research and development interactions with multiple industry partners (http://www.icr.ac.uk/working-with-industry/about-the-enterprise-unit). Both the authors and The Institute of Cancer Research may benefit from this. The authors thank Cancer Research UK (grant number C309/A11566) and The Institute of Cancer Research for funding canSAR. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.",
year = "2016",
month = sep,
doi = "10.4155/fmc-2016-0133",
language = "English (US)",
volume = "8",
journal = "Future Medicinal Chemistry",
issn = "1756-8919",
publisher = "Future Science",
number = "14",
}