Hypothesis Generation with Open Data and Explainable Algorithms

About this Class

Speaker: Rangan Sreenivas Sukumar Distinguished Technologist Hewlett Packard Enterprise (HPE) Abstract: In March of 2020, the “Force for Good” pledge of intellectual property to fight COVID-19 brought into action HPE products, resources and expertise to the problem of drug/vaccine discovery. Several scientists engaged in collaborations with HPE volunteers to accelerate efforts towards a drug/vaccine. This talk documents the spirit and outcome of such a collaboration of domain and data science and as an example of how artificial intelligence (AI), when applied with explainable context is augmented intelligence – one that empowers human experts to excel at their best by doing what computers do best. More specifically, we will demonstrate AI augmenting experts on hypothesis generation tasks by connecting and reasoning with a curated knowledge universe of medical facts and data. We explain the construction of a knowledge graph from 13 open datasets such as PubChem, UniProt, CHEMBL, RCSB, ClinicalTrials.gov  etc. (30 TBs in size with 150 billion medical facts/properties) and present the power of a massively parallel-processing database for interactive and exploratory discovery from multi-modal data (protein sequences, knowledge facts, and tables). On this knowledge graph we will show the ability to search for the “what-is”, “what-if”, “what-else” and the “what-could-be” using reasoning algorithms. We will show results from queries capable of comparing protein-sequences (~4 million comparisons per query in under a minute), and explain how one scientist during one of our hackathons was able to look for common proteins in COVID-19 (and newer variants) in other sequenced viruses, bacteria and fungi, search for previously-studied protein activity in other organisms and further extrapolate that knowledge to known protein-ligand activity from clinical trials data. This curiosity established a workflow for drug repurposing using our knowledge graph that serendipitously discovered the connection between Tetanus and COVID-19 posing the question - “Is Tetanus vaccination contributing to reduced severity of the COVID-19 infection?”.  We will conclude this talk with a live demo, encouraging domain and data scientists to pose questions beyond COVID-19 on this massive knowledge graph and engaging with our team for further collaboration. Join ZoomGov Meeting https://nih.zoomgov.com/j/1617561452?pwd=bE5YOVgrL2tHbFZidUJQOWZzdGlpZz09 Meeting ID: 161 756 1452 Passcode: 586729 One tap mobile +16692545252,,1617561452#,,,,*586729# US (San Jose) +16468287666,,1617561452#,,,,*586729# US (New York)