We are a diverse, interdisciplinary team who likes to tackle important questions in systems medicine by developing and using novel machine learning algorithms. 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.

CausalMGM: causal discovery on mixed data types


WordCloud of Benos Lab research


Evaluating undetermined low dose CT scan nodules


Representative Recent publications

  1. T. Cruz†, M. Jia†, J.C. Sembrat, T. Tabib, D.A.A. Vignali, P. Sanchez, R. Lafyatis, A.L. Mora, P. Benos, M. Rojas, “Reduced Proportion and Activity of Natural Killer Cells in the Lung of Patients with Idiopathic Pulmonary Fibrosis”, Am J Resp Crit Care Med (2021) 204:608-610. [Abstract] [Article]   †equal contribution
  2. 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]
  3. 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
  4. 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]
  5. 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

News & Highlights

Congratulations to Haiyi for his first author…

Category: News

Congratulations to Haiyi Mao for the acceptance of his first author paper to the proceedings track of the Machine Learning for Health (ML4H) 2022…

ML4H paper figure

Congratulations Marissa, Haiyi and Mark for your…

Category: News

Marissa, Haiyi, and Mark successfully defended their thesis proposals this month.  The theses are about developing methods for identifying…

New lab members - Marissa, Mark, Haiyi

Congratulations to Minxue, Daniel and Tyler for…

Category: News

A collaborative paper of three members of the Benos Lab was accepted for publication by the journal Frontiers in Epidemiology. The paper is…

Fig from the Frontiers Epic paper