Innovative AI Technologies
Revealing genomic causes of each individual tumor using tumor-specific causal inference (TCI) (Patent pending)
Discovering tumor-specific intercellular communication network using single-cell sequencing, spatial sequencing, and instance-specific Bayesian causal network learning.
Understanding individual tumor's disease mechanisms, such as oncogenic processes and immune evasion mechanisms.
Interpretable deep learning technology for inferring the state of cellular signaling systems and immune systems in a tumor
Reliable decision support systems to match cancer cells with effective drugs
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
Cai C, Cooper GF, Lu KN, Ma X, Xu S, Zhao Z, Chen X, Xue Y, Lee AV, Clark N, Chen V, Lu S, Chen L, Yu L, Hochheiser HS, Jiang X, Wang QJ, Lu X. (2019) Systematic discovery of the functional Impact of somatic genome alterations in individual tumors through tumor-specific causal inference. PLoS Computational Biology. 15(7): e1007088
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.
Xue, Y., Ding, MQ., Lu, X (2020) Learning to encode cellular responses to systematic perturbations with deep generative models. Systems Biology and Applications 6:35
Chen, X., Chen, L., et al (2021) An instance-specific causal framework for learning intercellular communication networks that define microenvironment of individual tumors. Preprint available at SSRN: https://ssrn.com/abstract=3925258 or http://dx.doi.org/10.2139/ssrn.3925258
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 (to appear)