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Developing integrative approaches for identifying disease-causing genes and dysfunctional networks

开发综合方法来识别致病基因和功能失调的网络

基本信息

批准号：
8880075
负责人：
Mary Yang
金额：
$ 37.35万
依托单位：
UNIVERSITY OF ARKANSAS AT LITTLE ROCK
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-07-01 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8880075
关键词：
Affect Bioinformatics Biological Biological Process Cancer Etiology Cataloging Catalogs Clinical Clinical Data Communities Complex DNA DNA Resequencing DNA Sequence Alteration DNA-Protein Interaction Data Disease Drug Targeting Early Diagnosis Early treatment Educational process of instructing Epigenetic Process Foundations Gene Cluster Gene Expression Gene Mutation Gene Proteins Genes Genome Genomics Goals Heterogeneity Human Investigation Lead Learning Machine Learning Malignant Neoplasms Malignant neoplasm of ovary Methods Modeling Modification Molecular Molecular Profiling Mutation Outcome Pathway interactions Performance Phosphorylation Population Proteins Recurrent disease Research Research Training Resources Role Sampling Signal Pathway Signal Transduction Single Nucleotide Polymorphism Statistical Models Students System Technology The Cancer Genome Atlas Universities base computer based statistical methods computer framework differential expression disease diagnosis disorder risk experience genome wide association study genome-wide genomic variation heuristics improved innovation insight knowledge base new therapeutic target next generation sequencing novel novel strategies personalized medicine predictive modeling programs promoter prospective protein protein interaction public health relevance scientific computing skills treatment planning tumor progression

项目摘要

DESCRIPTION: Cancer is a consequence of the accumulation of genetic alterations. Large whole-genome scale resequencing projects such as The Cancer Genome Atlas (TCGA) have been launched in an effort to comprehensively catalog the genomic mutations and epigenetic modifications that are associated with cancer. It is essential to identify cancer-causing genes and pathways to gain insight into the disease mechanisms and hence facilitate early diagnosis and optimal treatment. However, identifying cancer-causing genes and their functional pathways remains challenging due to the complex biological interactions and the heterogeneity of diseases. Genetic mutations in disease-causing genes can disturb signaling pathways that impact the expression of a set of genes performing certain biological functions. We refer to a set of such genes as a functional module. We hypothesize that driver mutations, that is, mutations that lead to cancer progression, are likely to affect common disease-associated functional modules, and the causal relationship between the mutations and the perturbed signals of the modules can be reconstructed from gene expression data and protein interaction data. In this project, we will develop a novel approach to infer disease-causing genes and networks by integrating information from multiple types of data including genomic variations, gene expression and protein interactions. We first dynamically identify disease-associated modules that consist of a set of interacting genes, then develop a Bayesian-based approach to infer causative genes from the disease-associated modules. Then, by developing a stochastic search based method, we can determine the paths connecting causative genes and gene modules. As a result, disease- related pathways are inferred from the paths. Furthermore, we will integrate those pathways with the human interactome to discover higher-level disease-associated networks. In addition, we will develop machine learning based classifiers to predict disease types and clinical outcomes utilizing the molecular signatures identified in this project, such as differentially expressed gene modules and causative genes. Our computational framework and classifiers will be made available to the research community via a webserver. The PI serves as the university bioinformatics program director and has extensive teaching and research experience. A goal of this project is also to provide scientific research training to students and o help students to gain biological insight through their involvement with the project. Students will learn practical scientific computing skills from the PI and develop their own computational approaches to solving specific biomedical problems under the guidance of the PI. Thus the project will serve as an effective learning-research model in bioinformatics.

描述：癌症是基因改变累积的结果。已经启动了大型全基因组重测序项目，如癌症基因组图谱(TCGA)，以努力全面编目与癌症有关的基因组突变和表观遗传修饰。识别致癌基因和致癌途径对于深入了解疾病机制，从而促进早期诊断和最佳治疗是至关重要的。然而，由于复杂的生物相互作用和疾病的异质性，识别致癌基因及其功能途径仍然具有挑战性。致病基因的基因突变可以干扰信号通路，从而影响一组执行某些生物功能的基因的表达。我们把一组这样的基因称为一个功能模块。我们假设，驱动突变，即导致癌症进展的突变，可能会影响常见的疾病相关功能模块，并且这些突变和模块的扰动信号之间的因果关系可以从基因表达数据和蛋白质相互作用数据中重建。在这个项目中，我们将开发一种新的方法来推断致病基因和网络，通过整合来自多种类型的数据的信息，包括基因组变异、基因表达和蛋白质相互作用。我们首先动态识别由一组相互作用的基因组成的疾病相关模块，然后开发一种基于贝叶斯的方法来从疾病相关模块中推断致病基因。然后，通过发展一种基于随机搜索的方法，我们可以确定连接致病基因和基因模块的路径。因此，可以从这些路径中推断出与疾病相关的路径。此外，我们将把这些途径与人类相互作用组结合起来，以发现更高水平的疾病相关网络。此外，我们将开发基于机器学习的分类器，利用本项目中确定的分子签名，如差异表达基因模块和致病基因，预测疾病类型和临床结果。我们的计算框架和分类器将通过网络服务器提供给研究界。PI担任大学生物信息学项目主任，具有丰富的教学和研究经验。该项目的一个目标也是为学生提供科学研究培训，并帮助学生通过参与该项目获得生物学洞察力。学生将从PI中学习实用的科学计算技能，并在PI的指导下开发自己的计算方法来解决特定的生物医学问题。因此，该项目将成为生物信息学中一种有效的学习研究模式。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identification of genes and pathways involved in kidney renal clear cell carcinoma.

DOI：
10.1186/1471-2105-15-s17-s2
发表时间：
2014
期刊：
BMC bioinformatics
影响因子：
3
作者：
Yang W;Yoshigoe K;Qin X;Liu JS;Yang JY;Niemierko A;Deng Y;Liu Y;Dunker A;Chen Z;Wang L;Xu D;Arabnia HR;Tong W;Yang M
通讯作者：
Yang M

A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study.