Developing novel heuristic methods for integrative computational biology

开发综合计算生物学的新颖启发式方法

• 批准号: 203833-2013
• 负责人: Jurisica, Igor
• 金额: $ 3.21万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546409
• 关键词: Developing; novel; heuristic; methods; integrative

Many theoretically excellent algorithms are inadequate for the high-throughput biological domains, due to the scale or complexity of the problem, or due to unrealistic assumptions. Forming intelligent hypotheses and developing computational models of biological systems without the possibility to evaluate them limits the potential to derive realistic models. We propose to improve scalability, robustness, sensitivity and specificity of pattern discovery and prediction algorithms, and integrate them to support a methodical approach to the "systems biology" analysis and visualization of high-throughput data in cancer research and intelligent decision support in medical informatics.

由于问题的规模或复杂性或由于不切实际的假设，许多理论上出色的算法对于高通量生物域而言不足。形成智能假设并开发生物系统的计算模型，而无需评估它们的可能性限制了推导现实模型的潜力。我们建议提高模式发现和预测算法的可伸缩性，鲁棒性，敏感性和特异性，并将它们整合起来，以支持对“系统生物学”分析和可视化癌症研究中高通量数据的有条理方法，以及医学信息学中的智能决策支持。