Novel methods for integrative computational biology

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Current challenges in biomedical research include information overload: the need to combine vast amounts of structured and unstructured information, and the need to identify, characterize, optimize, link, validate and interpret patterns in complex data. The majority of biological data and relationships between biological entities is conveniently modeled as a graph. Graph databases provide a high-performance solution to integrating diverse entities and providing a scalable platform for machine learning and inference. Data mining, graph theory and ontologies are essential components of successful learning systems. Quality, coverage and annotation of these typed graphs provide the necessary platform for model and hypotheses generation.******We propose to develop a scalable, distributed architecture to store, analyze integrated graph data to support advance machine learning and data modeling. This resource will provide comprehensive coverage of available proteome, federate and map entities to a database of experimentally detected, orthologous, and predicted tissue- and condition-specific protein interactions. We will improve and extended our association mining algorithm, FpClass, a machine learning system for weighted link prediction between interacting proteins. We will extend the method to predict links of various types between genes and proteins under the integrated graphical model in sequence, tissue, and condition-specific contexts using the Turing Complete and performance-optimized graph query language Gremlin.******Ontologies will be used to support multiple viewpoints and contexts. Graph theory and databases will provide necessary infrastructure and complex pattern inference platform. Machine learning and data mining will contribute new models, while probabilistic modeling will support handling incomplete, contradictory and ambiguous information. We will use NAViGaTOR to support visual data mining and interactive exploration of the typed graphs.******We will test and validate developed platforms using multiple, publicly available datasets. This research will generate novel algorithms, and after validation, their application will lead to improved understanding of complex diseases on a molecular level. Algorithms, results and relevant data will be made publicly available to encourage further computational research in this increasingly important area. Together with deep learning, these approaches are revolutionizing application areas since the performance of these systems frequently exceed domain experts' abilities and biological assay sensitivity and false discovery rates.******The proposed research will advance computational approaches and their applicability to high-throughput systems biology applications. An important function of this proposal is the training of bioinformatics professionals for which there is still great unfulfilled demand.**