BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/Los_Angeles X-WR-TIMEZONE:America/Los_Angeles BEGIN:VEVENT UID:300@escience.washington.edu DTSTART;TZID=America/Los_Angeles:20260122T163000 DTEND;TZID=America/Los_Angeles:20260122T172000 DTSTAMP:20260114T230204Z URL:https://escience.washington.edu/events/uw-data-science-seminar-ike-kek u/ SUMMARY:UW Data Science Seminar: Ike Keku DESCRIPTION:Please join us for a UW Data Science Seminar featuring UW Bioen gineering PhD Student Ike Keku on Thursday\, January 22nd from 4:30 to 5:2 0 p.m. PT. The seminar will be held in IEB G109.\n\n \;\n"Using AI/ML Models to Predict Cross-Family Ligand Binding to VEGFR"\nAbstract: Angiog enesis\, the formation of new blood vessels\, is tightly regulated by vasc ular endothelial growth factor receptors (VEGFRs). This process is fundame ntal to both physiological processes (e.g.\, wound healing\, tissue regene ration) and disease states (e.g.\, cancer progression\, diabetic retinopat hy\, atherosclerosis). Despite clinical success\, anti-VEGF therapies face resistance and limited durability. This limitation is partly due to the f act that angiogenic signaling involves more than a single ligand–recepto r family. In addition to VEGF\, platelet-derived growth factors (PDGFs) ca n also bind to VEGFRs. These interactions may affect endothelial cell beha vior and treatment effectiveness. Still\, these cross-family interactions are not fully understood. Because experimental protein-protein interaction (PPI) determination is time-consuming and costly\, computational approach es hold promise for large-scale interactome studies.\n\nEvaluating PPIs wi th various sequence-structural machine learning (ML) classifier combinatio ns could help uncover proteins that interact across families\, with the ad vantage of greater availability and accessibility compared to sequence dat a or structural data alone. Existing models frequently lack standardized e valuation measures and instruments for comparison. To close this gap\, we propose PPInsight\, a Python-based benchmarking and visualization tool tha t automates the extraction of reference interaction data\, runs various pr edictive models\, and produces comparative performance graphs. This approa ch will allow researchers to easily evaluate model correctness and consist ency. Through this\, the predictive performance of predictor models for PP Is between VEGFRs and cross-family ligands can be evaluated\, guiding expe rimental validation efforts and the downstream development of simulated va scularization models. By understanding the computational boundaries and st rengths of different model types\, this work may improve the prediction of uncharacterized ligand–receptor interactions and offer advancements in targeted therapy development for angiogenesis-dependent diseases.\n \; \n\nSpeaker Bio: Ike Keku is a Bioengineering PhD Student at the Universit y of Washington. He works in the Research Lab of Dr. Princess Imoukhuede\, where he studies systems biology\, data science\, and protein-protein int eractions. He completed his undergraduate work at the University of North Carolina at Chapel Hill\, earning a BS in Biomedical Engineering.\n\n  \;\nThe 2025-2026 seminars will be held in person\, and are free and open to the public.\n ATTACH;FMTTYPE=image/jpeg:https://escience.washington.edu/wp-content/uploa ds/2026/01/IkeKeku_headshotpic.jpeg END:VEVENT BEGIN:VTIMEZONE TZID:America/Los_Angeles X-LIC-LOCATION:America/Los_Angeles BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST END:STANDARD END:VTIMEZONE END:VCALENDAR