Integrating Genomic Data and Biological Knowledge to Learn Context-Specific Gene

整合基因组数据和生物知识来学习特定背景的基因

• 批准号: 7628429
• 负责人: Seungchan Kim
• 金额: $ 17.14万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2010-12-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7628429
• 关键词: Algorithms; Behavior; Bioinformatics; Biological; Biology; Cell physiology; Cells; Clinical; Clinical Data; Collaborations; Complex; Computer Simulation; Computer software; Computing Methodologies; Data; Development; Diagnosis; Disease; Drug Delivery Systems; Engineering; Funding; Genes; Genome; Genomics; Glioblastoma; Goals; Heterogeneity; Knowledge; Lead; Learning; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Measures; Methods; Molecular; Multiple Myeloma; Pancreas; Pathologic; Patient Care; Patients; Pattern; Performance; Pharmaceutical Preparations; Population Heterogeneity; Prognostic Marker; Regulation; Regulator Genes; Research; Research Personnel; Sampling; Signal Transduction; System; Tumor Subtype; United States National Institutes of Health; Ursidae Family; Validation; analytical tool; base; cancer cell; computer framework; computer science; conditioning; design; drug development; graphical user interface; improved; insight; interest; novel; novel therapeutics; tool; tumor

DESCRIPTION (provided by applicant): Cancer is a disease of considerable complexity. Cancer originates from and is supported by a wide variety of alterations in the genome that often lead to drastic alterations of the cell's control circuitry, producing a great deal of diversity in the molecular mechanisms operating in cancer cells. When attempting to make inferences from a diverse population, heterogeneity in either the network regulatory connections or operating rules blurs the relationships and rapidly reduces the ability to accurately discern regulatory interactions. This provides a considerable challenge to those attempting to determine which, of all the changes that can be seen, are consequential. However, knowledge of these regulatory changes will facilitate the discovery of prognostic markers and provide strong candidate drug targets. Current analytic methods rarely consider such complexity and heterogeneity. In addition, current methods to reverse-engineer regulatory mechanisms mostly concern a nearly homogeneous set of components that are nearly homogeneously co-regulated. This application proposes to develop computational methods that can search through heterogeneous sample sets to identify subsets of samples in which sets of genes that collaborate to carry out particular pathologic functions are homogeneously regulated. The method then utilizes identified subsets of samples with higher homogeneity to learn context-specific regulatory mechanisms. The method will use simultaneous analysis of a variety of molecular and clinical characterizations, and an analytic strategy that detects limited homogeneity of behavior against a background of high heterogeneity. The methods will be validated via subsequent analysis of combined genomic data, clinical information and available biological knowledge from three NIH funded projects which include multiple myeloma, glioblastoma and pancreatic cancers. Developed algorithms will be implemented as a set of computer software with graphical user-interface, which will be publicly available. This project is intended to produce types of analysis that are specifically designed to identify collaborative molecular behaviors in cancer via simultaneous analysis of multiple types of biomedical data, and to make them available to biomedical researchers, in the form of easily utilized software so that anyone with these types of biomedical data can use the methods developed in this project. Even a modest improvement in the ability to predict what patients would benefit from what treatments would significantly improve patient care. Understandings that would identify sets of synergistic drugs could have even higher impact.

描述(申请人提供)：癌症是一种相当复杂的疾病。癌症起源于基因组中的各种变化，并得到这些变化的支持，这些变化往往会导致细胞控制电路的剧烈变化，从而在癌细胞中运行的分子机制产生极大的多样性。当试图从不同的人群中做出推断时，网络监管连接或操作规则中的异质性会模糊关系，并迅速降低准确识别监管互动的能力。这给那些试图确定在所有可以看到的变化中哪些是相应的变化的人带来了相当大的挑战。然而，了解这些调控变化将有助于发现预后标志物，并提供强有力的候选药物靶点。目前的分析方法很少考虑这种复杂性和异质性。此外，目前对调控机制进行反向工程的方法大多涉及一组几乎同质的组件，这些组件几乎是同质的共同调控的。这项申请建议开发计算方法，可以在不同的样本集中搜索，以识别样本子集，在这些样本子集中，协作执行特定病理功能的基因集受到同质性调控。然后，该方法利用所识别的具有较高同质性的样本子集来学习特定于上下文的调节机制。该方法将同时分析各种分子和临床特征，并采用一种分析策略，在高度异质性的背景下检测行为的有限同质性。这些方法将通过随后对来自NIH资助的三个项目的基因组数据、临床信息和可用的生物学知识的分析来验证，这些项目包括多发性骨髓瘤、胶质母细胞瘤和胰腺癌。开发的算法将作为一套具有图形用户界面的计算机软件实施，并将公开提供。 该项目旨在生产专门设计的分析类型，通过同时分析多种类型的生物医学数据来识别癌症中的协作分子行为，并以易于使用的软件的形式将其提供给生物医学研究人员，以便任何拥有这些类型生物医学数据的人都可以使用在该项目中开发的方法。即使在预测哪些患者将从哪些治疗中受益的能力方面略有改善，也会显著改善患者的护理。如果达成共识，将确定多组协同作用药物，则可能产生更大的影响。