Progress with Knowledge Enriched Data Analytics
Explanatory-artificial-intelligence (x-AI) has highlighted the growing need to integrate knowledge types into big data analytics. During this seminar, Dr. Bernhald Palsson will describe the three approaches used to progress knowledge in this field:
- The use of Independent Component Analysis (ICA) to define independently modulated sets of genes in bacterial transcriptomes
- The use of pangenome analysis for the thousands of bacterial genome sequences being generated
- The use of machine learning methods for the analysis of antimicrobial resistance
Each of the approaches highlights an important advancement in the integration of knowledge types into x-AI. The first approach illustrates the principle of "getting answers to questions not asked"; the second shows "what is learned with scale"; and the third approach explains how mechanisms are built into genome-wide association studies (GWAS) using flux balance analysis (FBA).
Dr. Bernhald Palsson is the Distinguished Galletti Professor of Bioengineering, the principal investigator of the Systems Biology Research Group in the Department of Bioengineering, and professor of pediatrics at the University of California, San Diego.