Technology

Innovative AI Technologies

DeepRx COLOXIS. A first-of-the-class AI clinical decision system for predicting oxaliplatin benefits in GI cancers.

Tumor-specific causal inference. A patent technology called tumor-specific causal inference (TCI) for revealing genomic causes of each tumor using (US Patent awarded)

DeepRx IO. An AI system for discovering tumor-specific intercellular communication networks using single-cell sequencing, spatial sequencing, and instance-specific Bayesian causal network learning.

DeepRx GI: An AI system for guiding pan-gastrointestinal cancer therapies. Trained with data from thousands of patients from rigorous clinical trials, the system recommends optimal combination regimens for treating GI cancer patients.

DeepRx BRCA. An AI system for guiding pre-operation chemotherapies for breast cancer patients to increase the chance of reducing tumor burden and success of operation.

Publications

Ren, S., Cooper, GF., Chen, L., and Lu, X. (2024) An interpretable deep learning framework for genome-informed precision oncology. Nature Machine Intelligence. DOI: https://doi.org/10.1038/s42256-024-00866-y. A reprint is available here.
Chen, L, Wang, Y, Cai, C, Ding, Y, Kim, RS., Lipchik, C, Gavin, PG., Yothers, G, Allegra, CJ, Petrelli, NJ, Suga, JM., Hopkins, JO., Saito, NG., Evans, T., Jujjavarapu, S., Wolmark, N., Lucas, PC., Paik, S., Sun, M., Pogue-Geile, KL., Lu, X. (2023) Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer. Journal of Clinical Oncology Epub:JCO2301080. doi: 10.1200/JCO.23.01080
Ren, S., Tao, Y., Xue., Y., Yu., K., Schwartz, R., and Lu, X. (2022) De novo prediction of cell-drug sensitivities using deep learning-based graph-regularized matrix factorization. Proceedings of Pacific Symposium of Biocomputing 27:278-289
Chen, X., Chen, L., Kürten, C. H., Jabbari, F., Vujanovic, L., Ding, Y., ... & Lu, X. (2022). An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors. PLOS Computational Biology, 18(12), e1010761.
Tao, Y., Cai, C., Cohen, W., and Lu, X (2020) From genome to phenome: Predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer. Proceedings of Pacific Symposium on Biocomputing.
Tao, Y., Ren, S., Ding, MQ., Schwartz, R., Lu, X (2020) Predicting drug sensitivity of cancer cell lines via collaborative filtering with contextual attention. Proceedings of the Machine Learning for Healthcare Conference 2020. 126:660-684.
Ding, MQ., Chen, L., Cooper, GF., Young, JD., and Lu, X. (2017) Precision oncology beyond targeted therapy: Combining omics data with machine learning matches the majority of cancer cells to effective therapeutics. Molecular Cancer Research 16(2):269-278

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