About the session

In disease research, understanding the key genes involved and how they interact to drive disease occurrence or severity is extremely valuable. Discovering previously unknown gene-disease relationships helps us gain insights to develop new potential therapies. To this end, we applied machine learning (ML) to our QIAGEN Knowledge Graph (QKG) to predict novel gene-disease associations. A research team at MicroMatrices, who study familial paraganglioma, investigated potential new targets by identifying differentially expressed genes using laser dissection targeted transcriptomics analysis in tumor vs. normal tissue. They compared the changes found in their case study with predicted gene alterations made by our ML process and found potential new drug targets and candidate drugs. Their treatment hypotheses can be tested in a 3D model of paraganglioma using SpheroMatrices microtissue array (microTMA) technology.

If you'd like to explore a new way to identify potential new drug targets, you won't want to miss this unique opportunity to learn from industry experts about:

  • How machine learning used known relationships in the QKG to infer hidden relationships between potential targets and diseases
  • A library of 1500 disease networks available in QIAGEN Ingenuity Pathway Analysis (IPA)
  • MicroMatrices' research into paraganglioma and evaluation of novel gene associations predicted by our disease network
  • Opportunities to verify new drug targets for paraganglioma as well as potential targets in other diseases

Speakers

Simon Plummer
MicroMatrices
Dr. Plummer holds a PhD in biochemical pharmacology. He has served in various research roles throughout his career focusing on high-throughput sequencing and bioinformatics interpretation of data. Dr. Plummer joined MicroMatrices Associates Ltd. in 2011 and currently serves as Managing Director.
Jeff Green
Jeff Green holds a degree from Stanford University and is a software engineer at QIAGEN. He joined QIAGEN 16 years ago through its acquisition of Ingenuity Systems in 2013. Jeff is an integral part of the QIAGEN Ingenuity Pathway Analysis application development team and currently serves as the IPA Technical Lead. Jeff also served on the Project Insights team, which leverages machine learning methods to extend our understanding of biological systems, networks, and pathways.