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
- 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]
- 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]
- A.J. Sedgewick, K. Buschur, I. Shi, J.D. Ramsey, V.K. Raghu, D.V. Manatakis, Y. Zhang, J. Bon, D. Chandra, C. Karoleski, F.C. Sciurba, P. Spirtes, C. Glymour, P.V. Benos, “Mixed Graphical Models for Integrative Causal Analysis with Application to Chronic Lung Disease Diagnosis and Prognosis”, Bioinformatics (2019) 35:1204-1212. [Abstract] [Article]
- D.V. Manatakis, V.K. Raghu, and P.V. Benos, “piMGM: Incorporating Multi-Source Priors in Mixed Graphical Models for Learning Disease Networks” Bioinformatics (2018) 34:i848–i856. (Proc 2018 ECCB) [Abstract] [Article] [PMC version]
- G.D. Kitsios, A. Fitch, D.V. Manatakis, S. Rapport, K. Li, S. Qin, J. Huwe, Y. Zhang, Y. Doi, J. Evankovich, W. Bain, J.S. Lee, B. Methe, P.V. Benos, A. Morris, B. McVerry, “Respiratory microbiome profiling for etiologic diagnosis of pneumonia in mechanically ventilated patients”, Frontiers in Microbiol (2018) 9:1413. [Abstract] [Article] [PMC version]