Causal Discovery Algorithms for Translational Research with High-Throughput Data

用于高通量数据转化研究的因果发现算法

• 批准号: 7643514
• 负责人: Constantin F. Aliferis
• 金额: $ 0.74万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7643514
• 关键词: AKT1 gene; AKT2 gene; AKT3 gene; Address; Affect; Algorithms; Area; Arts; Benchmarking; Bioinformatics; Biologic Characteristic; Biological Markers; Biology; Biometry; Book Chapters; Books; Cancer cell line; Causations; Characteristics; Clinic; Clinical; Clinical Data; Clinical Trials; Communities; Computational Biology; Computer software; Computing Methodologies; Consultations; Data; Data Set; Depth; Development; Diagnosis; Diagnostic; Dimensions; Discipline; Disease; Drug Design; Educational process of instructing; Educational workshop; Engineering; Ensure; Epidermal Growth Factor Receptor; European; Evaluation; Event; Excision; Gene Expression; Gene Targeting; Genomics; Goals; Gold; Healthcare; Hereditary Disease; Home environment; Human; Human Cell Line; Inferior; Information Retrieval; Institution; International; Knowledge; Laboratories; Lead; Learning; Light; Localized; Machine Learning; Malignant neoplasm of lung; Marker Discovery; Medicine; Methods; Modality; Molecular; Molecular Profiling; Neighborhoods; Noise; Numbers; Online Systems; Outcome; Output; Paper; Pathway interactions; Peer Review; Performance; Pharmaceutical Preparations; Process; Proteomics; Protocols documentation; Public Domains; Publishing; Quality Control; Random Allocation; Randomized; Rate; Research; Research Personnel; Research Proposals; Role; Sample Size; Sampling; Schedule; Score; Services; Simulate; Solutions; Standards of Weights and Measures; Structure; Testing; Text; Thinking; Tissues; Translational Research; Variant; Work; base; c-erbB-1 Proto-Oncogenes; clinically relevant; computer based statistical methods; computer science; contextual factors; coping; data mining; design; drug development; heuristics; human data; human tissue; improved; innovation; insight; journal article; member; new technology; next generation; novel; novel diagnostics; outcome forecast; reconstruction; research study; software systems; symposium; theories; tool

Project Summary Causal Discovery Algorithms for Translational Research with High-Throughput Data The long-term goal of this project is to provide to the biomedical community next-generation causal algorithms to facilitate discovery of disease molecular pathways and causative as well as predictive biomarkers and molecular signatures from high-throughput data. Such knowledge and methods are necessary toward earlier and more accurate diagnosis and prognosis, personalized medicine, and rational drug design. If successful, the proposed research will have significant and wide methodological and practical implications spanning several areas of biomedicine with a primary focus and immediate benefits in high-throughput diagnostics and personalized medicine. It will provide significantly improved computational methods and deeper theoretical understanding related to producing molecular signatures and understanding mechanisms of disease and concomitant leads for new drugs. It will provide evidence about applicability of novel causal methods in other types of data. It will generate insights in specific pathways of lung cancer in humans. It will deepen our understanding and solutions to the Rashomon effect in ¿omics¿ data. The proposed research will also shed light on the operational value of the stability heuristic. Finally the research will engage the international research community to address open computational causal discovery problems relevant to high-throughput and other biomedical data. ¿ Aim 1. Evaluate and characterize several novel causal algorithms for biomarker selection, molecular signature creation and reverse network engineering using real, simulated, resimulated, and experimental datasets. Study generality of the methods by means of applicability to non-¿omics¿ datasets. ¿ Aim 2. Evaluate and characterize, novel and state of the art causal algorithms against state-of-the-art non-causal and quasi-causal algorithms. ¿ Aim 3. Systematically investigate the Rashomon effect as it applies to biomarker and signature multiplicity. ¿ Aim 4. Systematically investigate the utility of applying the stability heuristic for causal discovery. ¿ Aim 5. Derive novel biomarkers, pathways and hypotheses for lung cancer. ¿ Aim 6. Induce novel solutions through an international causal discovery competition. ¿ Aim 7. Disseminate findings.

项目总结