University of Pittsburgh
Department of Biomedical Informatics
Dr. Chen’s research concentrates on developing machine learning methods, especially deep learning models (DLMs) (e.g., Deep Neural Networks, Boltzmann Machine, and topic modeling), to study cancer cell signaling systems, cell-cell communication in tumor microenvironment (TME), heterogeneity in diseases, disease mechanisms and cancer pharmacogenomics. Dr. Chen uses the concise representation learned from the DLM with the causal inference to guide the identification of molecular signatures/biomarkers and predicts the clinical outcomes including drug sensitivity and patient survival. Based on Dr. Chen’s strong research background in bioinformatics, biomedical informatics, biology and machine learning, she successfully develops comprehensive AI models that precisely represent the state of signaling systems in cancer cells and use such information to improve the tumor-specific precision medicine (precision oncology).
The transcriptome of individual cells is a mixture of genes regulated by active cellular signaling pathways, and gene expression models serve as the readout for downstream transcriptomic changes. A signaling pathway could be activated through the interaction of ligand and receptor (LR). Our lab aims to discover such signals and communications by
1) advancing the nHDP model tailored for inferring transcriptomic programs of individual cells and
2) developing discovery algorithms for searching population-wide cell-cell interactions mediated by LR interaction.
Each instance (a tissue sample) has its unique cell composition and an instance-specific CCCN. Our lab aims to deconvolute the signals in the transcriptomes using high-resolution signals learned from single-cell transcriptomics. We build individualized causal model to detect the CCCNs in each instance and make the model more transparent and biologically explainable by incorporating the knowledge of biological components into the model. The identification of patterns in CCCNs could be used as informative features to explain tissue environment heterogeneity and evaluate their utility in precision medicine.
The integration of multiple data modalities in cancer research presents a promising avenue for improving patient outcomes through complementary features. By developing sophisticated deep learning architectures that can effectively combine and analyze these heterogeneous data sources, we aim to identify robust predictive signatures that could not be detected through single-modality analysis alone. The model learns to recognize complex patterns and relationships across these different data types, potentially uncovering novel biomarkers and improving our ability to predict treatment responses and patient outcomes. This integrated approach could significantly enhance clinical decision-making by providing more comprehensive and accurate predictions, ultimately leading to more personalized treatment strategies in cancer care.