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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570009
• 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. The long term goal is to develop and then apply novel tools for the integration, analysis and interpretation of complex biomedical data with aim to identify testable hypothesis and build useful models. The short term goals include 1) developing scalable, probabilistic, network-based algorithm for comprehensive identification of effective biomarkers for early disease detection, improved diagnosis and prognosis, and treatment response prediction; 2) developing scalable network inference approaches to predict combination treatment options using drug target databases and screens combined with networks of physical and functional protein interactions; 3) developing planning approaches for drug synthesis. Using combination of heuristic algorithms and machine learning based parameter optimization will help to reduce search space. Probabilistic modeling will help to handle incomplete, contradictory and ambiguous information in an automated fashion. Ontologies will be used to support multiple viewpoints and contexts. Additional attention will be paid to ensure the tools are interactive, seamlessly integrate diverse data sources, and they have to scale to ultra-high dimensions, support multimodal and rapidly evolving representations, and handle incompleteness of domain theories. Performance evaluation will be carried out on cancer informatics applications, using multiple publicly available datasets. The results of this research will not only generate novel algorithms, but importantly, their application will lead to fathoming cancer at a molecular level, eventually improving quality and reducing cost of cancer diagnosis and treatment by identifying prognostic and predictive signatures that enable tailoring treatment to each individual patient. The research will advance computational approaches and their applicability to high-throughput systems biology applications. An important function of this proposal is the training of bioinformatics professionals for which there is still a severe deficit. We will release tools and resources for free academic use to enable even broader benefit.

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。 长期目标是开发并应用新颖的工具，以集成，分析和解释复杂的生物医学数据，旨在识别可检验的假设并构建有用的模型。短期目标包括1）开发可扩展的，概率的，基于网络的算法，以全面鉴定有效的生物标志物，以提高早期疾病检测，改善诊断和预后以及治疗反应预测； 2）开发可扩展的网络推理方法，使用药物目标数据库和筛选结合了物理和功能蛋白质相互作用的网络来预测组合治疗方案； 3）开发药物合成的计划方法。 使用启发式算法和基于机器学习的参数优化的组合将有助于减少搜索空间。概率建模将有助于以自动化方式处理不完整，矛盾和模棱两可的信息。本体将用于支持多个观点和上下文。将要引起更多的关注，以确保工具是互动的，无缝整合了各种数据源，并且必须扩展到超高维度，支持多模式和快速发展的表示形式，并处理域理论的不完整。 绩效评估将在癌症信息应用上进行，使用多个公开 可用数据集。这项研究的结果不仅会产生新颖的算法，而且重要的是，它们的应用将在分子水平上导致癌症的癌症，最终通过识别预后和预测性特征来提高质量并降低癌症诊断和治疗的成本，从而促进每个患者的治疗。 该研究将推进计算方法及其对高通量系统生物学应用的适用性。该提案的一个重要功能是培训生物信息学专业人员仍然存在严重赤字的培训。我们将发布工具和资源以免费学术用途，以使更广泛 益处。