Causal Discovery Algorithms for Translational Research with High-Throughput Data

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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.