Computer Laboratory, University of Cambridge Cambridge, UK ; Department of Computer Science and Engineering, Pabna University of Science and Technology Pabna, Bangladesh ; Bone Biology, Garvan Institute of Medical Research, The University of New South Wales Sydney, NSW, Australia.
Computer Laboratory, University of Cambridge Cambridge, UK.
Front Cell Dev Biol. 2015 Jun 24;3:28. doi: 10.3389/fcell.2015.00028. eCollection 2015.
Multiple diseases (acute or chronic events) occur together in a patient, which refers to the disease comorbidities, because of the multi ways associations among diseases. Due to shared genetic, molecular, environmental, and lifestyle-based risk factors, many diseases are comorbid in the same patient. Methods for integrating multiple types of omics data play an important role to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Moreover, integrated omics and clinical information may potentially improve prediction accuracy of disease comorbidities. However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis. With the availability of the wide spread huge omics, phenotype and ontology information, it is becoming more and more practical to help doctors in clinical diagnostics and comorbidity prediction by providing appropriate software tool. We developed an R software POGO to compute novel estimators of the disease comorbidity risks and patient stratification. Starting from an initial diagnosis, omics and clinical data of a patient the software identifies the association risk of disease comorbidities. The input of this software is the initial diagnosis of a patient and the output provides evidence of disease comorbidities. The functions of POGO offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high-throughput and clinical data analysis pipelines. POGO is compliant with the Bioconductor standard and it is freely available at www.cl.cam.ac.uk/~mam211/POGO/.
多种疾病（急性或慢性事件）同时发生在一个患者身上，这是指疾病的共病，因为疾病之间存在多种关联。由于共同的遗传、分子、环境和基于生活方式的风险因素，许多疾病在同一患者中同时发生。整合多种类型的组学数据的方法对于识别整合的生物标志物以将患者分层为具有不同临床结局的组中起着重要作用。此外，整合的组学和临床信息可能潜在地提高疾病共病的预测准确性。然而，缺乏有效的和高效的生物信息学和统计软件来进行真正的综合数据分析。随着广泛的大规模组学、表型和本体论信息的可用性，通过提供适当的软件工具，帮助医生进行临床诊断和共病预测变得越来越实际。我们开发了一种 R 软件 POGO 来计算疾病共病风险和患者分层的新估计量。从患者的初始诊断、组学和临床数据开始，该软件识别疾病共病的关联风险。该软件的输入是患者的初始诊断，输出提供疾病共病的证据。POGO 的功能为诊断应用程序提供了预测疾病共病的灵活性，并且可以轻松集成到高通量和临床数据分析管道中。POGO 符合 Bioconductor 标准，可在 www.cl.cam.ac.uk/~mam211/POGO/ 上免费获得。
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