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Department of Electrical Engineering ESAT-SCD and IBBT-KU Leuven Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. yves.moreau@esat. kuleuven.be
At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.
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