Dataset Information

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.

SUBMITTER: Moni MA

PROVIDER: S-EPMC4081507 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

comoR: a software for disease comorbidity risk assessment.

Moni Mohammad Ali MA Liò Pietro P

Journal of clinical bioinformatics 20140523

<h4>Background</h4>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.<h4>Results</h4>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 ...[more]

PMID: 25045465

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