We are a computational group specializing in applications of machine learning in medicine.  Our ultimate goal is to identify risk factors and mechanisms affecting aging and 
contributing to the onset and progression of chronic diseases and cancer.  We also develop predictive methods and tools that can directly improve health.  We use probabilistic graphical models and other machine learning methods to integrate and mine high-dimensional, multi-modal biomedical data and to investigate biological processes pertinent to health and disease.


Representative Recent Publications

  1. E. Valenzi, H. Yang, J.C. Sembrat, L. Yang, S. Winters, R. Nettles, D.J. Kass, S. Qin, X. Wang, M. Myerburg, B. Methe, A. Fitch, J. Alder, P.V. Benos, B.J. McVerry, M. Rojas, A. Morris, G.D. Kitsios, “Topographic Heterogeneity of Lung Microbiota in End-Stage Idiopathic Pulmonary Fibrosis: The Microbiome in Lung Explants-2 (MiLEs-2) Study”,   Thorax (2021) 76:239-247. [Abstract] [Article]  [medRxiv]
  2. X. Ge†, V.K. Raghu†, P.K. Chrysanthis, P.V. Benos, “CausalMGM: an interactive web-based causal discovery tool”,   Nucleic Acids Research (2020) 48(W1):W597-W602. [Abstract] [Article]  [Web tool]   †equal contribution
  3. K.L. Buschur, M. Chikina, P.V. Benos, “Causal network perturbations for instance-specific analysis of single cell and disease samples”,   Bioinformatics (2020) 36:2515–2521. [Abstract] [Article] [PMC version]
  4. V.K. Raghu, W. Zhao, J. Pu, J.K. Leader, R. Wang, J. Herman, J.-M. Yuan, P.V. Benos*, D.O. Wilson, “Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models”,   Thorax (2019) 74:643-649. [Abstract]  [Article]  [PMC version]  *corresponding author
  5. C. Morse, T. Tabib, J. Sembrat, K.L. Buschur, H.T. Bittar, E. Valenzi, Y. Jiang, D.J. Kass, K. Gibson, W. Chen, A. Mora, P.V. Benos, M. Rojas, R. Lafyatis, “Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis”, European Respiratory Journal (2019) 54:1802441.  [Abstract]  [Article]