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How to build personalized multi-omics comorbidity profiles.

Moni MA, Liò P - Front Cell Dev Biol (2015)

Bottom Line: However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis.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/.

View Article: PubMed Central - PubMed

Affiliation: 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.

ABSTRACT
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/.

No MeSH data available.


Related in: MedlinePlus

Comparative representation of dysregulated genes and lifestyle impact on the disease comorbidity. (A) Represents an example of only dysregulated genetic influences on diseases with good lifestyle. (B) Shows an example of only bad lifestyle influences on diseases with no genetic variation. (C) Represents the combined impacts of lifestyle and dysregulated genes on diseases. Here, the circular red nodes nodes represent diseases name, triangular blue nodes represent genes symbols and square green nodes represent lifestyle factors. The size of the nodes represents the degree of associations.
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Figure 11: Comparative representation of dysregulated genes and lifestyle impact on the disease comorbidity. (A) Represents an example of only dysregulated genetic influences on diseases with good lifestyle. (B) Shows an example of only bad lifestyle influences on diseases with no genetic variation. (C) Represents the combined impacts of lifestyle and dysregulated genes on diseases. Here, the circular red nodes nodes represent diseases name, triangular blue nodes represent genes symbols and square green nodes represent lifestyle factors. The size of the nodes represents the degree of associations.

Mentions: We incorporated verified data from different data source with our software. Data integration reduces noise associated with each experimental limitation, thus increases sensitivity and specificity to detect true association relationships which results in less number of false positives. By integrating different types of omics and clinical data can produce more reliable predictions with increased sensitivity and specificity for detecting true functional disease comorbidity associations. This can help in finding the hidden connections between complex diseases. Such connections between complex diseases reflect common biological pathways and biological functions that may become manifest in the form of comorbidity. For an example, we show a comparative representation of dysregulated genes and lifestyle impact on the disease comorbidity in Figure 11. Here, panel A (see Figure 11A) represents an example of only dysregulated genetic influences on diseases with good lifestyle. Panel B (see Figure 11B) shows an example of only bad lifestyle influences on diseases with no genetic variation. Panel C (see Figure 11C) represents the combined impacts of lifestyle and dysregulated genes on diseases. Here, we observed that the combined impact of both lifestyle and dysregulated genes influences more and multiway on the diseases and disease comorbidities. It is conceivable that by integrating the data ranging from genotype to multiple levels of phenotypes, more precise and robust stratification of the patients with clinical outcome difference can be achieved.


How to build personalized multi-omics comorbidity profiles.

Moni MA, Liò P - Front Cell Dev Biol (2015)

Comparative representation of dysregulated genes and lifestyle impact on the disease comorbidity. (A) Represents an example of only dysregulated genetic influences on diseases with good lifestyle. (B) Shows an example of only bad lifestyle influences on diseases with no genetic variation. (C) Represents the combined impacts of lifestyle and dysregulated genes on diseases. Here, the circular red nodes nodes represent diseases name, triangular blue nodes represent genes symbols and square green nodes represent lifestyle factors. The size of the nodes represents the degree of associations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4478898&req=5

Figure 11: Comparative representation of dysregulated genes and lifestyle impact on the disease comorbidity. (A) Represents an example of only dysregulated genetic influences on diseases with good lifestyle. (B) Shows an example of only bad lifestyle influences on diseases with no genetic variation. (C) Represents the combined impacts of lifestyle and dysregulated genes on diseases. Here, the circular red nodes nodes represent diseases name, triangular blue nodes represent genes symbols and square green nodes represent lifestyle factors. The size of the nodes represents the degree of associations.
Mentions: We incorporated verified data from different data source with our software. Data integration reduces noise associated with each experimental limitation, thus increases sensitivity and specificity to detect true association relationships which results in less number of false positives. By integrating different types of omics and clinical data can produce more reliable predictions with increased sensitivity and specificity for detecting true functional disease comorbidity associations. This can help in finding the hidden connections between complex diseases. Such connections between complex diseases reflect common biological pathways and biological functions that may become manifest in the form of comorbidity. For an example, we show a comparative representation of dysregulated genes and lifestyle impact on the disease comorbidity in Figure 11. Here, panel A (see Figure 11A) represents an example of only dysregulated genetic influences on diseases with good lifestyle. Panel B (see Figure 11B) shows an example of only bad lifestyle influences on diseases with no genetic variation. Panel C (see Figure 11C) represents the combined impacts of lifestyle and dysregulated genes on diseases. Here, we observed that the combined impact of both lifestyle and dysregulated genes influences more and multiway on the diseases and disease comorbidities. It is conceivable that by integrating the data ranging from genotype to multiple levels of phenotypes, more precise and robust stratification of the patients with clinical outcome difference can be achieved.

Bottom Line: However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis.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/.

View Article: PubMed Central - PubMed

Affiliation: 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.

ABSTRACT
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/.

No MeSH data available.


Related in: MedlinePlus