The long-term objective of the Ideker Lab is to create artificially intelligent, mechanistic models of cancer and neurodegenerative diseases for translation of patient data to precision diagnosis and treatment. We seek to advance this goal by addressing fundamental questions in the field: What are the genetic and molecular networks that promote disease, and how do we best chart these? How do we use knowledge of these networks in intelligent systems for predicting the effects of genotype on phenotype?
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Meet the Team
The Ideker Laboratory has welcomed many distinguished researchers over the years — ranging from undergraduates to visiting scientists. Click buttons below to view Dr. Ideker’s profile and meet our past and present lab members.
Open Projects and Positions
The Ideker Laboratory is in continual open recruitment for excellent postdoctoral scholars, graduate students, and software engineers.
News
New Publication Alert
Rare and common variants associated with alcohol consumption identify a conserved molecular network
Our latest study explores the genetic basis of alcohol consumption by examining both common and rare genetic variants, uncovering a shared molecular network involved in alcohol metabolism and neuropsychiatric traits. This integrated approach provides a comprehensive understanding of the genetic factors influencing alcohol use, highlighting potential targets for future research and intervention.
Read the full study here.
Recent Publication
In vitro evolution and whole genome analysis to study chemotherapy drug resistance in haploid human cells
We released an exciting new publication on the use of haploid human cells to study drug resistance mechanisms. This research helps us understand how cancer fights back against treatment and could lead to better therapies.
Check out the publication here.
Cell Maps for Artificial Intelligence
AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines
Read our recent publication. This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools, status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health’s (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.
Drugst.One
Drugst.One — a plug-and-play solution for online systems medicine and network-based drug repurposing
Drugst.One transforms specialized computational medicine tools into user-friendly, web-based utilities for drug repurposing, enabling intuitive modeling and analysis of protein-drug-disease networks with just three lines of code. Successfully integrated with 21 systems medicine tools, Drugst.One streamlines drug discovery, allowing researchers to focus on essential pharmaceutical treatment research
Visit https://drugst.one for more information.
Proceedings of the National Academy of Sciences
Epigenetic age can fluctuate by five years in a single day
“It’s a study I’ve been waiting to see for a long time.” – Trey Ideker
Epigenetic changes—in particular, DNA methylation, which can regulate gene expression—are thought to accumulate over years or decades, “but not over short periods,” says Art Petronis, a chrono-epigeneticist with dual appointments at the University of Toronto, Canada, and Vilnius University in Lithuania. But in a recent study in Aging Cell, Petronis and coauthors found that epigenetic age actually fluctuates throughout the course of a day.
Amy McDermott
May 3, 2024
Simulated Chemistry: New AI Platform Designs Tomorrow’s Cancer Drugs
The new platform helped UC San Diego scientists synthesize 32 potential multi-target cancer drug
Explore the advancements at UC San Diego with our recent publication, where our innovative AI platform is revolutionizing cancer drug development. Dive deeper into how this technology is setting new standards in drug design and pharmaceutical sciences.
Related Press:
- Drug Discovery News, UC San Diego Today, list23, GlobeEcho., International The News, Scitechdaily
- “AI-guided drug discovery has become a very active area in industry, but unlike the methods being developed in companies, we’re making our technology open source and accessible to anybody who wants to use it.” –Trey Ideker, Ph.D.
UCSD School of Medicine uses AI to predict which drugs to use to treat cancer patients
Discover our recent publication highlighted by four simultaneous news coverage articles. Delve into an informative interview with Dr. Ideker and Abbie Black from CBS News.
Related Press:
- UC San Diego Today, Interesting Engineering, Inside Precision Medicine, Innovators Magazine, The UCSD Guardian, National Cancer Institute (NIH)
- “Hundreds of proteins work together in complex arrangements to replicate DNA. Mutations in any one part of this system can change how the entire tumor responds to chemotherapy.” – Trey Ideker, Ph.D.
Spotlight on Cancer Cell Map Initiative
A New Map
Smartly Done
Forbes 30 Under 30 Class of 2023
Yue Qin, et al. A multi-scale map of cell structure fusing protein images and interactions. Nature. 2021. [PubMed] [PDF]
Recent press: “Studies Delve Deep into the Protein Machinery of Cancer Cells.” NCI (4 Nov 2021)
A New Map Reveals the Complicated World in Which Cells Seek to Repair Damaged DNA.
Scott LaFee, UC San Diego Today. (2023 May 22) [PDF]
Nicole Mlynaryk, UC San Diego Today. (2023 May 18)
Congratulations to Dr. Yue Qin, Ideker Lab Graduate, for getting two major awards! Yue was selected to join the Forbes 30 Under 30, Class of 2023 (read more here and here) and also awarded the 2023 UCSD Chancellor’s Dissertation Medal for the Jacobs School of Engineering.
Yue Qin, et al. A multi-scale map of cell structure fusing protein images and interactions. Nature. 2021. [PubMed] [PDF]
Recent press: We Might Not Know Half of What’s in Our Cells, New AI Technique Reveals.
Tina Wang, et al. Quantitative Translation of Dog-to-Human Aging by Conserved Remodeling of the DNA Methylome. Cell Systems (2020 Aug). [PDF] [PubMed]
Recent Press: Old Dogs, New Research and the Secrets of Aging. New York Times (2020)
Brent M. Kuenzi, Jisoo Park, et al. Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell (2020 Oct). [PDF] [PubMed]
Recent Press: DrugCell: New Experimental AI Platform Matches Tumor to Best Drug Combo.
Dr. Ideker is a Clarivate Highly Cited Researcher for 4 straight years , from 2020 to 2023. This highly anticipated annual list identifies researchers who demonstrated significant influence in their chosen field or fields through the publication of multiple highly cited papers during the last decade. View the complete 2021 Highly Cited Researchers list.
Research Centers & Affiliates
