Directory
This Directory includes Faculty, Staff, Students, and Alumni selectable by category, search or alphabetical by last name. Over 110 students have graduated from the Department of Biomedical Informatics (25+ PhD, 50+ MS, 25+ Certificate). The diversity of careers available to DBMI alumnus is evident in their biographies. Many of our graduates are teaching and performing research in academic institutions, such as Vanderbilt University, Arizona State University, and New York University while others have entered private industry with companies such as Cerner Corporation and Boston Scientific; some have positions in government agencies, such as the NIH and AHRQ, while others are at major medical centers, serving in roles such as Chief Medical Information Officer. We maintain a database of the career paths of our graduates. If you are an alumnus, please contact us if you would like to submit or update information!
Lujia Chen
Biography
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.