Introduction

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.

Publications

  1. comoR: a software for disease comorbidity risk assessment.
    Cite this
    Moni MA, Liò P, 2014-01-01 - Journal of clinical bioinformatics

Credits

  1. Mohammad Ali Moni
    Developer

    Computer Laboratory, University of Cambridge, Bangladesh

  2. Pietro Liò
    Investigator

    Computer Laboratory, University of Cambridge, Bangladesh

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Summary
AccessionBT006470
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionBangladesh
Submitted ByPietro Liò