Causal Discovery Algorithms for Translational Research with High-Throughput DataThe long-term goal of this project is to provide to the biomedical community next-generation causalalgorithms to facilitate discovery of disease molecular pathways and causative as well as predictivebiomarkers and molecular signatures from high-throughput data. Such knowledge and methods arenecessary toward earlier and more accurate diagnosis and prognosis, personalized medicine, andrational drug design.If successful, the proposed research will have significant and wide methodological and practicalimplications spanning several areas of biomedicine with a primary focus and immediate benefits inhigh-throughput diagnostics and personalized medicine. It will provide significantly improvedcomputational methods and deeper theoretical understanding related to producing molecularsignatures and understanding mechanisms of disease and concomitant leads for new drugs. It willprovide evidence about applicability of novel causal methods in other types of data. It will generateinsights in specific pathways of lung cancer in humans. It will deepen our understanding and solutionsto the Rashomon effect in omics data. The proposed research will also shed light on the operationalvalue of the stability heuristic. Finally the research will engage the international research community toaddress open computational causal discovery problems relevant to high-throughput and otherbiomedical data.
Aim 1. Evaluate and characterize several novel causal algorithms for biomarkerselection, molecular signature creation and reverse network engineering using real, simulated,resimulated, and experimental datasets. Study generality of the methods by means ofapplicability to non- omics datasets.
Aim 2. Evaluate and characterize, novel and state of the art causal algorithms againststate-of-the-art non-causal and quasi-causal algorithms.
Aim 3. Systematically investigate the Rashomon effect as it applies to biomarker andsignature multiplicity.
Aim 4. Systematically investigate the utility of applying the stability heuristic forcausal 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.
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