Developing novel heuristic methods for integrative computational biology

由于问题的规模或复杂性，或者由于不切实际的假设，许多理论上优秀的算法不适合高通量生物领域。形成智能假设和开发生物系统的计算模型，而不可能对其进行评估，这限制了获得现实模型的潜力。我们建议提高模式发现和预测算法的可扩展性、鲁棒性、灵敏度和特异性，并将它们集成起来，以支持癌症研究中高通量数据的“系统生物学”分析和可视化以及医学信息学中的智能决策支持的系统方法。长期目标是开发并应用新的集成工具，分析和解释复杂的生物医学数据，旨在识别可验证的假设并建立有用的模型。近期目标包括：1）开发可扩展的、概率性的、基于网络的算法，用于全面鉴定用于早期疾病检测、改善诊断和预后以及治疗反应预测的有效生物标志物; 2）开发可扩展的网络推理方法，以使用药物靶标数据库和筛选结合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）开发药物合成的规划方法，将启发式算法与基于机器学习的参数优化相结合，有助于减少搜索空间。概率建模将有助于以自动化的方式处理不完整、矛盾和模糊的信息。本体将用于支持多个观点和上下文。另外，我们还将注意确保这些工具是交互式的，无缝集成不同的数据源，它们必须扩展到超高的维度，支持多模式和快速发展的表示，并处理领域理论的不完整性。这项研究的结果不仅将产生新的算法，更重要的是，它们的应用将导致在分子水平上深入了解癌症，最终通过识别预后和预测特征来提高癌症诊断和治疗的质量并降低成本，从而为每个患者提供定制治疗。这项研究将推进计算方法及其在高通量系统生物学应用中的适用性。这项建议的一个重要作用是培训生物信息学专业人员，而这方面的人才仍然严重不足。我们将发布免费学术使用的工具和资源，以实现更广泛的利益。

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 publiclyavailable 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 broaderbenefit.