Our ultimate goal is to investigate the risk factors and mechanisms contributing to the onset and progression of chronic diseases and cancer and develop predictive methods and tools that will improve health.  We use probabilistic graphical models and other machine learning methods to model biological processes 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.


Indicative Recent Work

  1. V.K. Raghu, A. Poon, P.V. Benos, “Evaluation of Causal Structure Learning Methods on Mixed Data Types”, PMLR, (2018), 92:48-65.  Abstract and pdf
  2. 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 (PubMed) and pdf   PMID:30042738.   PMCID:PMC6048198.
  3. 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 (PubMed) and pdf   PMID:29146185.   PMCID:PMC5827944
  4. D.A. Pociask, K.M. Robinson, K. Chen, K.J. McHugh, M.E. Clay, G.T. Huang, P.V. Benos, Y.M.W. Janssen-Heininger, J.K. Kolls, V. Anathy, J.F. Alcorn, “Epigenetic and Transcriptomic Regulation of Lung Repair during Recovery from Influenza Infection”, Am J Pathol, (2017), 187:851-863. Abstract (PubMed) and pdf. PMID: 28193481.  PMCID: PMC5397680
  5. A.J. Sedgewick, I. Shi, R.M. Donovan, P.V. Benos, “Learning mixed graphical models with separate sparsity parameters and stability-based model selection”, BMC Bioinformatics. (2016), 17 Suppl 5:175. Abstract (PubMed) and pdf.