Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Barcelona, Spain.
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Bioinformatics. 2018 Sep 15;34(18):3228-3230. doi: 10.1093/bioinformatics/bty315.
The study of comorbidities is a major priority due to their impact on life expectancy, quality of life and healthcare cost. The availability of electronic health records (EHRs) for data mining offers the opportunity to discover disease associations and comorbidity patterns from the clinical history of patients gathered during routine medical care. This opens the need for analytical tools for detection of disease comorbidities, including the investigation of their underlying genetic basis.
We present comoRbidity, an R package aimed at providing a systematic and comprehensive analysis of disease comorbidities from both the clinical and molecular perspectives. comoRbidity leverages from (i) user provided clinical data from EHR databases (the clinical comorbidity analysis) and (ii) genotype-phenotype information of the diseases under study (the molecular comorbidity analysis) for a comprehensive analysis of disease comorbidities. The clinical comorbidity analysis enables identifying significant disease comorbidities from clinical data, including sex and age stratification and temporal directionality analyses, while the molecular comorbidity analysis supports the generation of hypothesis on the underlying mechanisms of the disease comorbidities by exploring shared genes among disorders. The open-source comoRbidity package is a software tool aimed at expediting the integrative analysis of disease comorbidities by incorporating several analytical and visualization functions.
https://bitbucket.org/ibi_group/comorbidity.
Supplementary data are available at Bioinformatics online.
由于共病对预期寿命、生活质量和医疗保健成本的影响，对共病的研究是一个主要重点。电子健康记录 (EHR) 可用于数据挖掘，为从常规医疗护理过程中收集的患者临床病史中发现疾病关联和共病模式提供了机会。这就需要开发分析工具来检测疾病共病，包括调查其潜在的遗传基础。
我们提出了 comoRbidity，这是一个 R 包，旨在从临床和分子角度对疾病共病进行系统全面的分析。comoRbidity 利用（i）用户从 EHR 数据库提供的临床数据（临床共病分析）和（ii）研究疾病的基因型-表型信息（分子共病分析）来全面分析疾病共病。临床共病分析能够从临床数据中识别出显著的疾病共病，包括性别和年龄分层以及时间方向性分析，而分子共病分析则通过探索疾病之间的共享基因，支持对疾病共病潜在机制的假设生成。开源的 comoRbidity 包是一个软件工具，旨在通过整合多个分析和可视化功能，加速疾病共病的综合分析。
https://bitbucket.org/ibi_group/comorbidity。
补充数据可在生物信息学在线获得。
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