Systems and Data integration for Bioscience My research uses Systems Biology modeling and Data Mining approaches to aid in providing a better understanding of gene-disease interaction. Each of these two domains are limited in computing resources for a common research computing environment. The emergence of High Performance computing (HPC) infrastructures allow better handling of that dilemma. The long term goal is to develop a new decision support knowledgebase for gene-disease study. This uses integrative approaches (algorithm, process, tools) for life science multi-scale systems integration and analysis using a combination of big data mining, machine learning and Systems Biology approaches. This include 1) a novel reusable multi-scale data mining model for knowledge discovery based on adaptive machine learning and artificial intelligence; 2) new algorithms for data transformation and integration to handle the heterogeneity among the integrated data sources and Systems Biology data (images, text, relational data, etc.). 3) a flexible implementation easy to be use tools on a HPC and big data infrastructure using R Bioconductor, Matlab, Java J2EE; 4) several Biological use case studies development.
