Integrating context-specific networks to predict ataxia genes

整合上下文特定网络来预测共济失调基因

• 批准号: 8477601
• 负责人: Margit Burmeister
• 金额: $ 23.33万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8477601
• 关键词: Address; Algorithms; Animal Model; Ataxia; Basic Science; Bioinformatics; Biological Process; Brain; Candidate Disease Gene; Categories; Cerebellum; Clinical; Complement; DNA; Data; Data Set; Disease; Disease Pathway; Drug Formulations; Future; Gene Proteins; Genes; Genomics; Hand; Hereditary Disease; Human; Imagery; Inflammatory; Internet; Knowledge; Lead; Learning; Malignant Neoplasms; Methodology; Mining; Modeling; Mutation; Network-based; Phenotype; Probability; Process; Proteins; Research; Route; Specificity; Testing; Time; Tissues; Variant; Work; base; data integration; disease phenotype; functional genomics; human data; human disease; novel; protein B; prototype; public health relevance; tool

DESCRIPTION (provided by applicant): Integrating large-scale genomics data has huge potential to accelerate the identification of disease genes in human. Three major challenges lie in the current integrative approach for predicting disease genes. First, previous integrations in general limit genomic data input to one species at a time, while disease datasets are often generated in multiple model organisms. Second, public functional genomic datasets are dominated and biased by certain data types and accessible tissues, which can be addressed by expert curation of input datasets. Third, when multiple tissue-specific networks have been generated, a mathematical formulation is lacking to prioritize among these competing networks for the specific disease under consideration. This collaborative proposal aims at addressing the above challenges by exploring a prototype of bioinformatics tools to integrate multiple relevant global and tissue-specific networks across mammalian species targeting a specific disease, here ataxia. This proposal is based on our preliminary data in developing both global and cerebellum-specific networks to prioritize ataxia associated genes, and on the two PIs' complementary expertise in genomic data integration and experimental ataxia gene confirmation. We will 1) use domain-specific and multiple species data to establish global, brain, cerebellum, related tissue, and ataxia-specific networks, and develop web tools to explore these networks; and 2) develop multiple kernel learning algorithms to weigh and integrate multiple networks to predict ataxia-associated genes. Although the algorithms will be developed targeting ataxia only, we envision that this expert-driven integrative approach will be adaptable to other disease gene identification scenarios.

描述（由申请人提供）：