Lin Xu Lab

My primary research focus is to develop medical AI algorithms for identifying novel therapeutic targets and biomarkers of various human diseases. My Ph.D. training in Biostatistics and Bioinformatics at Cornell University gave me a strong foundation in the analysis of high-dimensional datasets, including deep learning, Bayesian modeling, and bioinformatics algorithm development. During my postdoctoral training, I extended this foundation into clinical and translational research. Now, as a tenure-track Assistant Professor at UT Southwestern, my lab leverages genomics, transcriptomics, epigenomics, single-cell, and spatial multi-omics data to uncover molecular mechanisms that drive disease initiation, progression, and therapeutic resistance.

Since establishing my laboratory at UT Southwestern in 2022, I have published 134 peer-reviewed research articles. My laboratory has developed computational algorithms that identify multi-omics-based clinical biomarkers and disease-associated genes from bulk, single-cell, and spatially resolved omics data, thereby advancing biological and translational insights. Together, these efforts have produced multiple deep learning models and statistical algorithms for identifying disease genes and therapeutic targets [e.g., Huang et al., Nature (2020); Lu et al., Nature Machine Intelligence (2022); Chai et al., Nature Medicine (2022); Takahiko et al., Science Translational Medicine (2022) (Cover Story); Shi et al., Cancer Cell (2022); Lebek et al., Science (2023); Xu et al., Nature Communications (2023); Zhu et al., Cancer Cell (2023); Zhang et al., Journal of Clinical Investigation (2023); He et al., Nature Communications (2023); Yu et al., Nature Communications (2023); Santos et al., Nature Communications (2023); Li et al., Nature Communications (2023); Wei et al., Cell (2023); Hernandez et al., PNAS (2024); Hu L et al., Molecular Cell (2024); Eglenen-Polat et al., Nature Communications (2024); Lin CC et al., Nature Communications (2024); Xu L et al., Nature Communications (2024); Venkateswaran N et al., Nature Communications (2024); Chen S et al., Nature Communications (2025); Liao C et al., Nature Communications (2025); Yang X et al., Nature Immunology (2025); Lu ZY et al., Nature Communications (2025); Zhou Y et al., Nature Communications (2025); Adachi Y et al., Journal of Clinical Investigation (2025); Napolitano F et al., Journal of Clinical Investigation (2025); Bann et al., Circulation (2025); Ding et al., Circulation (2025); Caravia XM et al., PNAS (2025); Guan P et al., PNAS (2025); Engreitz JM et al., Nature (2025); Huang HC, et al. Science Translational Medicine (2026); He et al. Science Immunology (2026); Wang et al. Genome Biology (2026); Shi DD, et al. Science Translational Medicine (2026); Sternisha et al. Science (2026); Abdullah KG, et al. Cell (2026)]. Since 2022, my lab's work has appeared in leading journals, including Nature, Science, Cell, Nature Medicine, Cancer Cell, eLife, PNAS, Nature Machine Intelligence, Nature Immunology, Nature Metabolism, Nature Communications, Science Translational Medicine, Science Advances, Molecular Cell, Genes & Development, Cell Reports, Journal of Clinical Investigation, Circulation, Circulation Research, and Developmental Cell.

Building on this record of computational-method development, my laboratory also pioneers applications of artificial intelligence (AI), particularly foundation models and large language models (LLMs), to accelerate biological and medical discovery. Our emphasis is on interpretable and generalizable AI frameworks that extend foundation models into genomics, epigenomics, and precision medicine. My laboratory has published multiple LLM-based algorithms for biological and medical applications at premier computer science conferences, including NeurIPS, ICML, AAAI, and ACL.

I have a successful mentoring records for students and trainees. Two of my co-mentored postdoctoral fellows have received NIH K99 Pathway to Independence Awards in 2024 and 2026, respectively. My graduate student also received a National Science Foundation Graduate Research Fellowship in 2023, and three of my postdoc and staff trainees was appointed as tenure-track Assistant Professors in the past five years.