Feb 23, 2024
Get to know the individuals behind the Ideker Lab. Discover available positions within our lab and explore career opportunities in bioinformatics, cancer cell research, and related disciplines at other organizations.
What the Ideker Lab is reading . . . The Network Biology journal club was established by the Ideker Lab in 2016 to review and evaluate scientific papers in the area of systems biology. All UCSD trainees and research scientists are welcome to attend these meetings, click link below to view upcoming meeting schedule and past papers.
Congratulations to Dr. Trey Ideker for his continued recognition as a 2024 Highly Cited Researcher.
UC San Diego ranks 9th in the world for Most Influential Researchers — with 56 recognitions— on the 2024 list.
UC San Diego Today
November 26, 2024
Simulated Chemistry: New AI Platform Designs Tomorrow’s Cancer Drugs
De novo generation of multi-target compounds using deep generative chemistry [NatureComm][PDF]
UC San Diego Today
May 06, 2024
Infusion of Artificial Intelligence in Biology
With deep learning methods revolutionizing life sciences, researchers bet on de novo proteins and cell mapping models to deliver customized precision medicines.
Feb 23, 2024
UC San Diego School of Medicine uses AI to predict which drugs to use to treat cancer patients.
Congratulations to Dr. Yue Qin! Yue was selected to join the Forbes 30 Under 30, Class of 2023 and also awarded the 2023 UCSD Chancellor’s Dissertation Medal for the Jacobs School of Engineering.
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)
Artificial intelligence is on its way to transforming how we understand and treat disease.
Nicole Mlynaryk
UC San Diego Today
May 18, 2023
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. 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
A note-worthy 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.
A multilineage screen identifies actionable synthetic lethal interactions in human cancers
Fong, et al. Nature Genetics 2024
Prediction of immunotherapy response using mutations to cancer protein assemblies
Kong, et al. Sciences Advances 2024
Interface-guided phenotyping of coding variants in the transcription factor RUNX1
Representing mutations for predicting cancer drug response
Rare and common variants associated with alcohol consumption identify a conserved molecular network
In vitro evolution and whole genome analysis to study chemotherapy drug resistance in haploid human cells
Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes
De novo generation of multi-target compounds using deep generative chemistry
The Ideker Lab is recruiting exceptional bioinformatics graduate students, postdocs, and senior research scientists to work across multiple laboratory projects. Prior to submitting an inquiry on available positions, please click the link below to learn more about how to apply.
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?