Evidence Evaluation in Biomedical Knowledge Graphs for Pharmaceutical Discovery

Authors

Jeremy J. Yang, University of New Mexico Internal Medicine Translational InformaticsFollow

Publication Date

10-5-2023

Abstract

In this seminar, Dr. Yang will describe several biomedical data science research projects from diverse domains, involving teams comprised of contributors from UNM and elsewhere, which share a common theme of evidence evaluation for pharmaceutical discovery. What is the strongest biomedical evidence about a disease for discovery of novel pharmaceutical therapies? This is a fundamental challenge for biomedical scientists, but also translates to a parallel question for data science: Can we systematically assemble and query biomedical knowledge graphs in a computational discovery platform guided by rational, algorithmic measures of relevance and confidence, facilitating scientific discovery? And, how have continuing waves of scientific and technological progress, in an era of bigger and bigger data, informed and empowered these inquiries?

Learning Objectives:

● Participants will be able to explain the meaning of "knowledge graph".

● Participants will be able to describe how data can be aggregated to rationally measure evidence.

Document Type

Video

Publisher

Health Sciences Library and Informatics Center - Biomedical Informatics Seminar Series

Language

English

Keywords

knowledge graph, informatics, pharmaceutical discovery, algorithmic measures

Description

This is the video recording of this seminar. To get access to the presentation slides, please go to https://zenodo.org/records/8417616.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Recommended Citation

Yang, Jeremy J.. "Evidence Evaluation in Biomedical Knowledge Graphs for Pharmaceutical Discovery." (2023). https://digitalrepository.unm.edu/bmi/23