Ideker, Trey

A gene ontology inferred from molecular networks

Dutkowski, J., Kramer, M., Surma, M. A., Balakrishnan, R., Cherry, J. M., Krogan, N. J., & Ideker, T. (2013) · Nature Biotechnology

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 184

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F. A., Hafner, M., Sharan, R., Peng, J., et al. (2021) · Nature Cancer

Computational Drug Discovery MethodsDomain Adaptation and Few-Shot Learning

Cited by 180

Visible Machine Learning for Biomedicine

Yu, M. K., Ma, J., Fisher, J., Kreisberg, J. F., Raphael, B. J., & Ideker, T. (2018) · Cell

Machine Learning in HealthcareExplainable Artificial Intelligence (XAI)

Cited by 162

Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data

Bandyopadhyay, S., Kelley, R., Krogan, N. J., & Ideker, T. (2008) · PLoS Computational Biology

Bioinformatics and Genomic NetworksGene Regulatory Network Analysis

Cited by 141

Genotype to phenotype via network analysis

Carter, H., Hofree, M., & Ideker, T. (2013) · Current Opinion in Genetics & Development

Bioinformatics and Genomic NetworksGenomics and Rare Diseases

Cited by 132

Rac2 Controls Tumor Growth, Metastasis and M1-M2 Macrophage Differentiation In Vivo

Joshi, S., Singh, A. R., Zulcic, M., Bao, L., Messer, K., Ideker, T., Dutkowski, J., & Durden, D. L. (2014) · PLoS ONE

Immune cells in cancerCancer Cells and Metastasis

Cited by 128

Evaluation of large language models for discovery of gene set function

Hu, M., Alkhairy, S., Lee, I., Pillich, R. T., Fong, D., Smith, K., Bachelder, R., Ideker, T., & Pratt, D. (2025) · Nature Methods

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 104

A multi-scale map of cell structure fusing protein images and interactions

Qin, Y., Huttlin, E. L., Winsnes, C. F., Gosztyla, M. L., Wacheul, L., Kelly, M. R., Blue, S. M., Zheng, F., Chen, M., Schaffer, L. V., et al. (2021) · Nature

Bioinformatics and Genomic NetworksAdvanced Proteomics Techniques and Applications

Cited by 82

Interpretation of cancer mutations using a multiscale map of protein systems

Zheng, F., Kelly, M. R., Ramms, D. J., Heintschel, M. L., Tao, K., Tutuncuoglu, B., Lee, J. J., Ono, K., Foussard, H., Chen, M., et al. (2021) · Science

Bioinformatics and Genomic NetworksCancer Genomics and Diagnostics

Cited by 81

Mapping the multiscale structure of biological systems

Schaffer, L. V., & Ideker, T. (2021) · Cell Systems

Bioinformatics and Genomic NetworksGene Regulatory Network Analysis

Cited by 75

Transcriptional regulation of protein complexes within and across species

Tan, K., Shlomi, T., Feizi, H., Ideker, T., & Sharan, R. (2007) · Proceedings of the National Academy of Sciences

Bioinformatics and Genomic NetworksGene Regulatory Network Analysis

Cited by 58

Somatic mutation as an explanation for epigenetic aging

Koch, Z., Li, A., Evans, D. S., Cummings, S., & Ideker, T. (2023) · Nature Aging

Epigenetics and DNA MethylationGenetics, Aging, and Longevity in Model Organisms

Cited by 44

State of the interactomes: an evaluation of molecular networks for generating biological insights

Wright, S. N., Colton, S., Schaffer, L. V., Pillich, R. T., Churas, C., Pratt, D., & Ideker, T. (2024) · Molecular Systems Biology

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 32

Active interaction mapping reveals the hierarchical organization of autophagy

Kramer, M. H., Farré, J.-C., Mitra, K., Yu, M. K., Ono, K., Demchak, B., Licon, K., Flagg, M., Balakrishnan, R., Cherry, J. M., et al. (2017) · Molecular Cell

Autophagy in Disease and TherapyFungal and yeast genetics research

Cited by 31

Transcriptional responses to DNA damage

Silva, E., & Ideker, T. (2019) · DNA repair

DNA Repair MechanismsGenomics and Chromatin Dynamics

Cited by 31

scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics

Bai, D., Mo, S., Zhang, R., Luo, Y., Gao, J., Yang, J. P., Wu, Q., Rahmani, H., Amariuta, T., Grotjahn, D., et al. (2026) · Nature Communications

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 15

An adversarial scheme for integrating multi-modal data on protein function

Nasser, R., Schaffer, L. V., Ideker, T., & Sharan, R. (2025) · Cell Systems

Bioinformatics and Genomic NetworksMachine Learning in Bioinformatics

Cited by 2