Integrative computational biology

目的：综合计算生物学是理解复杂疾病生物学和影响个体化医疗的必要平台。然而，孤立地推进计算工具不足以影响计算生物学。由于问题的规模或复杂性，或者由于使用不切实际的假设，许多理论上优秀的方法不适用于高通量生物领域。形成假设和发展生物系统的计算模型而不可能对其进行测试，限制了推导出现实模型的潜力。我们建议提高模式发现算法的可扩展性、鲁棒性、敏感性和特异性，并将它们集成在一起，以支持癌症研究中高通量数据的系统生物学分析和可视化的系统方法。

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.