title: comoR: a software for disease comorbidity risk assessment.
abstract: BACKGROUND
  The diagnosis of comorbidities, which refers to the coexistence of different acute and chronic diseases, is difficult due to the modern extreme specialisation of physicians. We envisage that a software dedicated to comorbidity diagnosis could result in an effective aid to the health practice.
  
  
  RESULTS
  We have developed an R software comoR to compute novel estimators of the disease comorbidity associations. Starting from an initial diagnosis, genetic and clinical data of a patient the software identifies the risk of disease comorbidity. Then it provides a pipeline with different causal inference packages (e.g. pcalg, qtlnet etc) to predict the causal relationship of diseases. It also provides a pipeline with network regression and survival analysis tools (e.g. Net-Cox, rbsurv etc) to predict more accurate survival probability of patients. The input of this software is the initial diagnosis for a patient and the output provides evidences of disease comorbidity mapping.
  
  
  CONCLUSIONS
  The functions of the comoR offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high-throughput and clinical data analysis pipelines.
authors.affiliation: Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK ; Department of Computer Science & Engineering, Pabna University of Science & Technology, Pabna, Bangladesh.
authors.forename: Mohammad Ali
authors.initials: MA
authors.lastname: Moni
authors.affiliation: Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.
authors.forename: Pietro
authors.initials: P
authors.lastname: Liò
keywords: Comorbidities; Disease associations; Relative risk