KnowEnG: A SCALABLE KNOWLEDGE ENGINE FOR LARGE SCALE GENOMIC DATA Charles Blatti Biomedical Applications Cancer Pharmacogenomics: Genomic profiling of high-risk breast cancer patients before and after drug therapy. Predict drug response from molecular and genetic profiling of patients and identify most essential signatures of response KnowEnG Goals Build and publicly deploy a Knowledge Engine for Genomics that enables users to perform analysis of their experimental data in the context of Big Data community knowledge sources. Leverage computational expertise in data mining, machine learning, and scalable/distributed learning technologies to develop data-driven cyberenvironments of the future for biomedical scientists and clinicians to generate and evaluate novel hypotheses and insights about their data. Comparative Transcriptomics: Genomic profiling of regions in brains of social animals during specific behaviors. Identify gene modules and transcription factors that are play a key role in social behavior and psychology About KnowEnG Genotype to Phenotype: Metabolic profiling and genomic sequencing of 500 strains of Actinomycetes bacteria Predict the strains that are likely to produce novel compounds with potential antibiotic activity KnowEnG is one of 11 Centers of Excellence in Big Data Computing funded by NIH in 2014 as part of their Big Data to Knowledge (BD2K) Initiative. It brings together researchers from the University of Illinois and the Mayo Clinic to design and test an E-science framework for genomics Components of the Knowledge Engine AIM 2: Develop efficient algorithms to analyze user experimental data in the form of a Analysis Matrix (AM) in the context of the KN Focus on core operations with general applicability to biological data sets including classification of AM columns, AM or KN-derived feature selection, simultaneous clustering of AM and KN, etc. I/UCRC Extensions AIM 1: Transform community data into massive, heterogeneous Knowledge Network (KN) Automatically extract entitles and relationships from relevant sources into KN representation including data from newly sequenced species and from high-throughput experimental assays AIM 3: Efficient implementation of algorithms that scale on rapidly growing KN. Ultimately deployed on commercial cloud platform and accessible to users worldwide AIM 4: Develop interface to guide user analysis methods and access to scalable computational resources Incorporate tools to visual results in context of KN and relate findings to existing literature