Ideker Lab

UC San Diego 
School of Medicine

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Meet the Team

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.

Network Biology
Journal Club

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.

Announcements

    Recent Publications

    A multilineage screen identifies actionable synthetic lethal interactions in human cancers

    Fong, et al. Nature Genetics 2024

    Putting proteins in context
    Hu M, et al. Cell Systems 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

     

    Join Our Team

    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. 

    Research Centers & Affiliates​

    Research Areas

    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?