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Dr. Etienne Z. Gnimpieba is a Research Assistant Professor of bioinformatics in the University of South Dakota (USD) Biomedical Engineering program. As faculty, Dr. Gnimpieba is involved in multiple research activities, including computational systems biology, and advanced cyberinfrastructure development for data analysis for bioscience and biomedicine knowledge discovery. He has published articles in Nucleic Acid Research, Molecular Cancer Research (AACR), Molecular Biosystems journal (RSC Publishing), Brain Sciences, Faseb journal, and several conference proceedings related to computer science usage in Bioscience and Biomedicine. Before Dr. Gnimpieba joined USD faculty, he received a Ph.D. in biotechnology and bioinformatics, an MS in computational engineering in Informatics and Mathematics for Integrative Biology, and an MS in computer science specialized on systems modeling, artificial intelligence, and human-machine interface.
Bioinformatics, systems biology, data mining, natural language processing, machine learning
My lab focus on Systems and Data integration for Bioscience and Biomedicine knowledge discovery. We develop intuitive data acquisition and management systems for relevant knowledge extraction. I use Systems Biology and Data Mining approaches to aid in providing a better understanding of biosystem-phenotype interaction (e.g. gene-disease, social behavior-drug, cell environment-protein expression...). The long-term goal is to develop new decision support knowledgebase for predictive and precision bioscience and biomedicine. We use 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 includes 1) a novel reusable multi-scale data manipulation model (collect, store, manage, mining) for knowledge discovery; based on adaptative intuitive user interface, 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 use tools on a HPC and big data infrastructure using R Bioconductor, Matlab, Java J2EE; 4) Computational Biology use cases development.
The lab's industry experience includes the development of data analysis workflows for clinical and biotechnology applications. This translational research allow us to collaborate with industry partners such as ABBOTT Pharma to develop a Systems Biology framework to study a drug mechanism using big data from High Throughput sequencing technologies. Data science Advisor at INANOVATE Inc., a biotechnology company specializing in therapy and diagnosis systems development. Working with INANOVATE, we developed new predictive systems for cancer and related disease diagnoses, using data science tools.
The lab activities also include bioinformatics training and outreach, I'm currently the Bioinformatics Educational Director of the SDBRIN (South Dakota Biomedical Research Infrastructure Network). The lab has mentored over 30 students in small research projects (high school, undergraduate, graduate, postdoc), and trained over 200 students through hands-on workshops such as our annual Bioinformatics Undergraduate Workshop Series. The lab's educational goal is to bridge the gap between math, computer science and biomedicine related STEM disciplines.