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News Release 15-141
Nomadic computing speeds up Big Data analytics
University of Texas researcher designs novel way to analyze bigger datasets using supercomputers and machine learning algorithms
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Schematic of the proposed approach to predicting gene-disease associations. First, the researchers construct gene and disease features using different sources. Then, they perform Inductive matrix completion using row and column features. The shaded region in the P matrix corresponds to genes or diseases with at least one known association
Credit: Nagarajan Natarajan and Inderjit Dhillon
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The local network around the human disease diabetes insipidus and two genes highly ranked by Catapult, AQP1 (top ranked candidate) and MYBL2 (ranked as number 40). AQP1 is ranked higher than MYBL2 because there are more paths from diabetes insipidus to AQP1 than to MYBL2, both through model organism phenotypes and through the gene--gene network. Only genes and phenotypes that are associated to both diabetes insipidus and the predicted genes AQP1 and MYBL2 are shown.
Credit: U. Martin Singh-Blom, Nagarajan Natarajan, Ambuj Tewari, John O. Woods, Inderjit S. Dhillon, Edward M. Marcotte
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Inderjit S. Dhillon, Gottesman Family Centennial Professor of Computer Science at The University of Texas at Austin and director of the Center for Big Data Analytics.
Credit: The University of Texas at Austin
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