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
A New Map
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]
Smarty Done
Nicole Mlynaryk, UC San Diego Today. (2023 May 18)
“The same way your Netflix algorithm predicts what movie you should watch tonight, our algorithms will predict what drug is best fit to treat your cancer”. – Dr. Trey Ideker
Forbes 30 Under 30
Class of 2023
Congratulations to Dr. Yue Qin, recent Ideker Lab Graduate, for getting two major awards this week! Yue has been 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.
Fan Zheng* & Mark Kelly*, et al. Interpretation of cancer mutations using a multiscale map of protein systems. Science. 2021 Oct 1. *Equal Contribution [PDF] [PubMed]
Kim M, et al. A protein interaction landscape of breast cancer. Science. 2021 Oct 1. [PDF] [PubMed]
Swaney DL, et al. A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity. Science. 2021 Oct 1. [PDF] [PubMed]
Related Press:
- “Studies Delve Deep into the Protein Machinery of Cancer Cells.” NCI (4 Nov 2021)
- “From COVID to cancer, gene-mapping tool could ‘revolutionize’ treatment“. SF Chronicle (2 Oct 2021)
- “Moonshot Project Aims to Understand and Beat Cancer Using Protein Maps“. Singularity Hub (5 Oct 2021)
- “Looking Beyond DNA to See Cancer with New Clarity,” features Trey Ideker, PhD.
- See also FierceBiotech, AXIOS, MSN, and Times of San Diego
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 two years in a row, in both 2020 and 2021. 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
