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Our ultimate goal is to identify risk factors and mechanisms contributing to the onset and progression of chronic diseases and cancer and to develop predictive methods and tools that will directly improve health.  We use probabilistic graphical models and other machine learning methods to investigate biological processes pertinent to disease and integrate and mine high-dimensional, multi-modal biomedical data.  We are very interested in the effect of gene regulatory networks and genotype in disease in combination with clinical data.

 

Representative Recent Publications

  1. 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) accepted.
  2. 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 (2018) accepted.  [Abstract]  [Article]
  3. 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]
  4. 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]
  5. V.K. Raghu, C.H. Beckwitt, K. Warita, A. Wells, P.V. Benos*, Z.N. Oltvai*, “Biomarker identification for statin sensitivity of cancer cell lines”, Biochem Biophys Res Commun (2018) 495:659-665.  [Abstract]  [Article]  [PMC version]