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Dr. Chen is an assistant professor in the Department of Biomedical Informatics at the University of Pittsburgh. Her research focuses on developing machine learning methods, especially deep learning and causal discovery models, to study cellular signaling systems, disease mechanisms, and pharmacogenomics. Recently, she has been focusing on using a combination of machine learning and causal discovery to unveil the individualized cell-cell communication networks (CCCNs) in the tissue environment. By studying varied CCCNs within/across each individual/subgroup, she can investigate the mechanisms of inter- and intra-heterogeneity.

Dr. Chen has established a strong research background in bioinformatics, biomedical informatics, machine learning, deep learning, molecular biology, immunology during her Ph.D. and postdoc studies. She has been developing computational methods for studying the cellular signaling transduction system since 2010. She has more than 10 years of experience in the genomic field with over 20 publications in Nature Medicine, Journal of Clinical Oncology, Nature Machine Intelligence, Science Advances, Science Signaling, Bioinformatics, and PLOS Computational Biology. She is especially proficient in bioinformatics and machine learning techniques. She is one of the first researchers who applied the deep learning model (DLM) and causal discovery to the genomic field and published several papers showing the feasibility of DLMs in analyzing multi-omics data. Her work on using DLM in biomedical domain was published as early as 2015.

Funding as PI
NIH NHGRI R01 Grant 3/2025 – 2/2030
NSF I-Corp Grant 4/2024 – 4/2025
NIH NLM K99/R00 11/2019 – 2/2025

Most recent publications
1. Zhang, H., Lu, B., Lu, X., Saeed, A., and Chen, L. (2024) Current transcriptome database and biomarker discovery for immunotherapy by immune checkpoint blockade. bioRxiv.
2. AI-Bzour, NN., AI-Bzour AN., Qasaymeh, A., Saeed, A., Chen, L, Saeed, A. (2024) Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma. Scientific Reports 14(1), 1-16.
3. Chen, L, et al. (2024) Machine learning predicts oxaliplatin benefit in early colon cancer. Journal of Clinical Oncology.
4. Shuang, X., Chen, L., Cooper, G., Lu, X., et al. (2024) An interpretable deep learning framework for genome-informed precision oncology. Nature Machine Intelligence.
5. Zhang, H., Lu, X., Chen, L. (2024) Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future. Future Oncology.
6. Zhang, H., Lu, X., Chen, L. (2023) scGEM: unveiling the nested tree-structured gene co-expressing modules in single cell transcriptome data. Cancers, 15 (17), 4277.
7. Sun, R., Zhao, H., Gao, D.S., Ni, A., Li, H., Chen, L., Lu, X., Chen, K. and Lu, B. (2023) Amphiregulin couples IL1RL1+ regulatory T cells and cancer-associated fibroblasts to impede antitumor immunity. Science Advances, 9(34), 7399.