Computational prediction of cancer-gene function

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Abstract
Many cancer genes remain functionally uncharacterized. Experimental methods to characterize their functions are inefficient, time consuming and expensive. The increasing availability of diverse molecular profiles and functional-interaction data make the prediction of cancer-gene functions possible. New computational prediction methods now enable the automated assessment of cancer-gene function. The main difficulties are how to simultaneously integrate different high-throughput data sources and dependably assign multiple functions to a cancer gene. Trustworthy gene annotations are crucial to achieving the best possible functional predictions for newly discovered or uncharacterized cancer genes. Rigorous evaluation of the accuracy of functional predictions generated by computational methods is vital for formulating biologically relevant hypotheses to direct further rounds of experimentation.