Molecular profiling data from scientific studies aiming for early detection and better management of diseases such as cancer, has accumulated at rates far beyond our abilities to efficiently extract knowledge of value to the practice of precisin medicine. A major challenge is that these data are often generated using multiple high-throughput technologies giving rise to panomics data such as gene expression and DNA methylation for the same or related classification task. This project will develop critically neede computational methods and tools for the integrative modeling of panomics data to improve disease state classification from related molecular profiling studies. This project will extend Transfer Rule Learning (TRL) methods that were previously developed to deal with sparse data from biomarker profiling studies, by automatically learning classification rules from one dataset, transferring that knowledge and using it when learning rules from a related dataset. This project will develop, apply and evaluate a novel method for knowledge transfer that involves the use of ontological or taxonomic hierarchies along with classification rule learning. Specifically, this project will test the hypothesis that transfer learning of classification rules using functional mapping (TRL-FM) via ontological structure to provide domain-specific relatedness improves integrative modeling of panomics data over conventional methods to yield better predictive performance and identify more robust biomarker panels for disease state classification. The TRL-FM prototype will be applied to existing de-identified panomics data from two diverse domains for the classification of (1) cancer, and (2) parasitic infections in global populations using microbiome profiling. The TRL-FM models will be validated for precise lung cancer classification and robust biomarker discovery, using an existing set of de-identified panomics data and related nodule size information from a cohort of high-risk CT-screened patients, and comprehensively compared to state-of-the-art classifiers. This project can help create more robust screening tools for the precise classification of lung cancer, the leading cause of death from cancer in the United States. This project will also impact global health with the potential to help improve screening and management of infections caused by helminths, the most common parasites affecting more than a billion people worldwide, using data obtained from fecal microbiome profiling. This project will result in computational tools that can efficiently integrat knowledge from multiple sources when building predictive models from panomics data. The predictive models are highly interpretable, capturing patterns that underlie subpopulations in the data, as classification rules with augmented information about the robustness of discriminative biomarkers. This project will create tools to benefit the rapidly growing human microbiome research community, by incorporating knowledge specific to the analyses of bacterial species sequenced from ribosomal RNA. The TRL-FM tools will make integrative modeling of microbiome data more efficient thereby enabling rapid insights into bacterial strains and species that harm or support human health.
This project will develop critically needed computational methods and tools for the integrative modeling of panomics data to improve disease state classification from related molecular profiling studies. This project can help create more robust screening tools for the precise classification of lung cancer, the leading cause of death from cancer in the United States. This project will also impact global health with the potential to help improve screening and management of infections caused by helminths, the most common parasites affecting more than a billion people worldwide, using data obtained from fecal microbiome profiling.
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