About this Class

Register Abstract: The need to integrate knowledge types into big data analytics, generally referred to as explanatory-artificial-intelligence (x-AI), is growing. This talk will describe progress with three approaches to such knowledge enrichment: 1) the use of Independent Component Analysis (ICA) to define independently modulated sets of genes in bacterial transcriptomes, 2) the use of pangenome analysis for the thousands of bacterial genome sequences being generated, and 3) the use of machine learning methods for the analysis of antimicrobial resistance. The first case illustrates the principle of ‘getting answers to questions not asked,’ the second case illuminates ‘what is learned with scale,’ and the third case shows how mechanisms are built into genome-wide association studies (GWAS) using flux balance analysis (FBA). Presenter: Bernhard Palsson, PhD Distinguished Galletti Professor of Bioengineering, Department of Bioengineering, UC San Diego Professor of Pediatrics, UC San Diego School of Medicine Meeting ID: 161 756 1452 Passcode: 586729 One tap mobile +16692545252,,1617561452#,,,,,,0#,,586729# US (San Jose) +16468287666,,1617561452#,,,,,,0#,,586729# US (New York) Dial by your location +1 669 254 5252 US (San Jose) +1 646 828 7666 US (New York) Meeting ID: 161 756 1452 Passcode: 586729 Find your local number https://nih.zoomgov.com/u/ayFfvRtd4 Join by SIP 1617561452@sip.zoomgov.com Join by H.323 161.199.138.10 (US West) 161.199.136.10 (US East) Meeting ID: 161 756 1452 Passcode: 586729 For questions, please contact Steve Tsang at steve.tsang@nih.gov