Integrative computational biology

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Objective: Integrative computational biology is a necessary platform to fathom biology of complex diseases and impact individualized medicine. However, advancing computational tools in isolation is insufficient to impact computational biology. Many theoretically excellent approaches are inadequate for the high-throughput biological domains, due to the scale or complexity of the problem, or due to using unrealistic assumptions. Forming hypotheses and developing computational models of biological systems without the possibility to test them limits the potential to derive realistic models. We propose to improve scalability robustness, sensitivity and specificity of pattern discovery algorithms, and integrate them to support a methodical approach to the systems biology analysis and visualization of high-throughput data in cancer research.