Equivalent Partial Correlation Methods for Integrative Genetic Network Analysis
综合遗传网络分析的等效偏相关方法
基本信息
- 批准号:9133431
- 负责人:
- 金额:$ 34.62万
- 依托单位:
- 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:2015-09-01 至 2018-05-31
- 项目状态:已结题
- 来源:
- 关键词:AgeAlgorithmsCell physiologyClinicalComputing MethodologiesCopy Number PolymorphismDataDiseaseGenderGenesGeneticHealthKnowledgeLightMalignant neoplasm of lungMeasuresMethodologyMethodsMethylationMicroRNAsMolecularMolecular ProfilingPathway AnalysisPatient CareRegulator GenesResearch PersonnelScientistSeriesStagingStatistical Methodsbasecancer typecomputer frameworkdata integrationflexibilityhigh throughput technologyimprovedinnovationmRNA Expressionnovelprotein expressiontherapeutic target
项目摘要
DESCRIPTION (provided by applicant): The emergence of high-throughput technologies has made it feasible to measure the activities of thousands of genes simultaneously, which provides scientists a major opportunity to infer global gene regulatory networks (GRNs). Accurate inference of GRNs is very important, which allows people to gain a systematic understanding of the molecular mechanism, to shed light on the mechanism of diseases that occur when cellular processed are dysregulated, and furthermore to identify potential therapeutic targets for diseases. Given the high dimensionality and high complexity of high-throughput data, inference of global GRNs largely relies on the advance of computational methods. However, the current computational methods for inference of global GRNs are either inaccurate or computationally infeasible. How to infer global GRNs has put a great challenge on the current statistical methodology. The investigators propose an equivalent measure of partial correlation coefficients, and based on which develop an innovative computational framework for inference of global GRNs. The new measure of partial correlation coefficients can be evaluated with a reduced conditional set and thus feasible for high dimensional problems. Under the new framework, the investigators develop a series of algorithms which are able to provide a comprehensive inference for global GRNs by integrating various types of high-throughput molecular profiling data, e.g., mRNA expression, copy number variation, methylation, microRNA, and protein expression, and adjusting with various clinical covariates, e.g., age, gender, and disease stage. The proposed algorithms are applied to infer the global GRNs for various types of cancer with a special focus on lung cancer, while they are applicable to all other
types of diseases. Statistically, this project proposes an innovative framework for inference of global GRNs, and the algorithms developed under which are not only computationally efficient, but also very flexible in data integration, covariate adjustment, prior knowledge integration, and network comparison. Biomedically, this is the first study to integrate such comprehensive and complementary information using rigorous statistical methods to study the global GRNs for various types of cancer.
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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FAMING LIANG其他文献
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{{ truncateString('FAMING LIANG', 18)}}的其他基金
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电子健康记录的随机深度学习:利用海量碎片数据进行本地化学习
- 批准号:
10793778 - 财政年份:2023
- 资助金额:
$ 34.62万 - 项目类别:
An Imputation-Consistency Algorithm for Biomedical Complex Data Analysis
生物医学复杂数据分析的插补一致性算法
- 批准号:
9658022 - 财政年份:2018
- 资助金额:
$ 34.62万 - 项目类别:
Equivalent Partial Correlation Methods for Integrative Genetic Network Analysis
综合遗传网络分析的等效偏相关方法
- 批准号:
9696111 - 财政年份:2015
- 资助金额:
$ 34.62万 - 项目类别:
Equivalent Partial Correlation Methods for Integrative Genetic Network Analysis
综合遗传网络分析的等效偏相关方法
- 批准号:
9273537 - 财政年份:2015
- 资助金额:
$ 34.62万 - 项目类别:
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