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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

Output figure and statistics of >comorbidityMap(“042”, “ICD9”). The icd-9-CM code of the HIV is 042, which is used as input to the comorbidityMap. We show disease comorbidity for the HIV infection (042) with other diseases, whose ICD-9-CM codes are 042.0 (with specified infections), 042.1 (causing other specified infections), 042.2 (with specified malignant neoplasms), 042.9 (acquired immunodeficiency syndrome, unspecified), 043 (HTLV-III/LAV infection), 043.1 (HTLV-III/LAV infection causing specified diseases of the central nervous system), 043.3 (HTLV-III/LAV infection causing other specified conditions), 043.9 (acquired immunodeficiency syndrome-related complex with or without other conditions), 044 (Other HTLV-III/LAV conditions), 044.9 (HTLV-III/LAV infection, not otherwise specified), 088 (arthropod-borne diseases), 117 (mycoses), 121.3 (fascioliasis), 130 (toxoplasmosis), 130.0 (meningoencephalitis due to toxoplasmosis), 130.8 (multisystemic disseminated toxoplasmosis), 136 (unspecified infectious and parasitic diseases), 136.3 (pneumocystosis), 137.1 (late effects of central nervous system tuberculosis), 176 (Kaposi's sarcoma), 299 (pervasive developmental disorders), 321 (type 2 diabetes mellitus), 363.10 (disseminated chorioretinitis), 429 (ill-defined descriptions and complications of heart disease), 795 (nonspecific abnormal cytological, histological, immunological, and dna test findings), and 795.8 (abnormal tumor markers). POGO uses color rectangle to classify different disease codes and the size of the rectangle is used to represent the severity of that disease.
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Figure 9: Output figure and statistics of >comorbidityMap(“042”, “ICD9”). The icd-9-CM code of the HIV is 042, which is used as input to the comorbidityMap. We show disease comorbidity for the HIV infection (042) with other diseases, whose ICD-9-CM codes are 042.0 (with specified infections), 042.1 (causing other specified infections), 042.2 (with specified malignant neoplasms), 042.9 (acquired immunodeficiency syndrome, unspecified), 043 (HTLV-III/LAV infection), 043.1 (HTLV-III/LAV infection causing specified diseases of the central nervous system), 043.3 (HTLV-III/LAV infection causing other specified conditions), 043.9 (acquired immunodeficiency syndrome-related complex with or without other conditions), 044 (Other HTLV-III/LAV conditions), 044.9 (HTLV-III/LAV infection, not otherwise specified), 088 (arthropod-borne diseases), 117 (mycoses), 121.3 (fascioliasis), 130 (toxoplasmosis), 130.0 (meningoencephalitis due to toxoplasmosis), 130.8 (multisystemic disseminated toxoplasmosis), 136 (unspecified infectious and parasitic diseases), 136.3 (pneumocystosis), 137.1 (late effects of central nervous system tuberculosis), 176 (Kaposi's sarcoma), 299 (pervasive developmental disorders), 321 (type 2 diabetes mellitus), 363.10 (disseminated chorioretinitis), 429 (ill-defined descriptions and complications of heart disease), 795 (nonspecific abnormal cytological, histological, immunological, and dna test findings), and 795.8 (abnormal tumor markers). POGO uses color rectangle to classify different disease codes and the size of the rectangle is used to represent the severity of that disease.

Mentions: Patient medical records contain important clarification regarding the co-occurrences of diseases affecting the same patient. Two diseases are connected if they are co-expressed in a significant number of patients in a population (Hidalgo et al., 2009). To estimate the correlation starting from disease co-occurrence, we need to quantify the strength of the comorbidity risk. We used two comorbidity measures to quantify the strength of comorbidity associations between two diseases: (i) the Relative Risk (fraction between the number of patients diagnosed with both diseases and random expectation based on disease prevalence) as the quantified measures of comorbidity tendency of two disease pairs; and (ii) ϕ-correlation (Pearsons correlation for binary variables) to measure the robustness of the comorbidity association (Moni and Lio, 2014). We used the relative risk RRij and ϕ-correlation ϕij of observing a pair of diseases i and j affecting the same patient. The RRij allows us to quantify the co-occurrence of disease pairs compared with the random expectation. When two diseases co-occur more frequently than expected by chance, we will get RRij > 1 and ϕij > 0. The two comorbidity measures are not completely independent of each other. We included links between disease pairs for which the co-occurrence is notably greater than the random expectation based on population prevalence of the diseases. Clinical information is from the http://www.icd9data.com in the ICD-9-CM format and collected from Hidalgo et al. (2009). The function comorbidityMap of POGO package is able to take input an OMIM id/3 or 5 digit ICD-9-CM code of a disease or a list of gene symbols/Entrez ids and provides comorbidity map of the patient based on the relative risk and ϕ-correlation. comorbidityMap requires two parameters: id list and id type. An example and its output is given in Figure 9.


How to build personalized multi-omics comorbidity profiles.

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

Output figure and statistics of >comorbidityMap(“042”, “ICD9”). The icd-9-CM code of the HIV is 042, which is used as input to the comorbidityMap. We show disease comorbidity for the HIV infection (042) with other diseases, whose ICD-9-CM codes are 042.0 (with specified infections), 042.1 (causing other specified infections), 042.2 (with specified malignant neoplasms), 042.9 (acquired immunodeficiency syndrome, unspecified), 043 (HTLV-III/LAV infection), 043.1 (HTLV-III/LAV infection causing specified diseases of the central nervous system), 043.3 (HTLV-III/LAV infection causing other specified conditions), 043.9 (acquired immunodeficiency syndrome-related complex with or without other conditions), 044 (Other HTLV-III/LAV conditions), 044.9 (HTLV-III/LAV infection, not otherwise specified), 088 (arthropod-borne diseases), 117 (mycoses), 121.3 (fascioliasis), 130 (toxoplasmosis), 130.0 (meningoencephalitis due to toxoplasmosis), 130.8 (multisystemic disseminated toxoplasmosis), 136 (unspecified infectious and parasitic diseases), 136.3 (pneumocystosis), 137.1 (late effects of central nervous system tuberculosis), 176 (Kaposi's sarcoma), 299 (pervasive developmental disorders), 321 (type 2 diabetes mellitus), 363.10 (disseminated chorioretinitis), 429 (ill-defined descriptions and complications of heart disease), 795 (nonspecific abnormal cytological, histological, immunological, and dna test findings), and 795.8 (abnormal tumor markers). POGO uses color rectangle to classify different disease codes and the size of the rectangle is used to represent the severity of that disease.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 9: Output figure and statistics of >comorbidityMap(“042”, “ICD9”). The icd-9-CM code of the HIV is 042, which is used as input to the comorbidityMap. We show disease comorbidity for the HIV infection (042) with other diseases, whose ICD-9-CM codes are 042.0 (with specified infections), 042.1 (causing other specified infections), 042.2 (with specified malignant neoplasms), 042.9 (acquired immunodeficiency syndrome, unspecified), 043 (HTLV-III/LAV infection), 043.1 (HTLV-III/LAV infection causing specified diseases of the central nervous system), 043.3 (HTLV-III/LAV infection causing other specified conditions), 043.9 (acquired immunodeficiency syndrome-related complex with or without other conditions), 044 (Other HTLV-III/LAV conditions), 044.9 (HTLV-III/LAV infection, not otherwise specified), 088 (arthropod-borne diseases), 117 (mycoses), 121.3 (fascioliasis), 130 (toxoplasmosis), 130.0 (meningoencephalitis due to toxoplasmosis), 130.8 (multisystemic disseminated toxoplasmosis), 136 (unspecified infectious and parasitic diseases), 136.3 (pneumocystosis), 137.1 (late effects of central nervous system tuberculosis), 176 (Kaposi's sarcoma), 299 (pervasive developmental disorders), 321 (type 2 diabetes mellitus), 363.10 (disseminated chorioretinitis), 429 (ill-defined descriptions and complications of heart disease), 795 (nonspecific abnormal cytological, histological, immunological, and dna test findings), and 795.8 (abnormal tumor markers). POGO uses color rectangle to classify different disease codes and the size of the rectangle is used to represent the severity of that disease.
Mentions: Patient medical records contain important clarification regarding the co-occurrences of diseases affecting the same patient. Two diseases are connected if they are co-expressed in a significant number of patients in a population (Hidalgo et al., 2009). To estimate the correlation starting from disease co-occurrence, we need to quantify the strength of the comorbidity risk. We used two comorbidity measures to quantify the strength of comorbidity associations between two diseases: (i) the Relative Risk (fraction between the number of patients diagnosed with both diseases and random expectation based on disease prevalence) as the quantified measures of comorbidity tendency of two disease pairs; and (ii) ϕ-correlation (Pearsons correlation for binary variables) to measure the robustness of the comorbidity association (Moni and Lio, 2014). We used the relative risk RRij and ϕ-correlation ϕij of observing a pair of diseases i and j affecting the same patient. The RRij allows us to quantify the co-occurrence of disease pairs compared with the random expectation. When two diseases co-occur more frequently than expected by chance, we will get RRij > 1 and ϕij > 0. The two comorbidity measures are not completely independent of each other. We included links between disease pairs for which the co-occurrence is notably greater than the random expectation based on population prevalence of the diseases. Clinical information is from the http://www.icd9data.com in the ICD-9-CM format and collected from Hidalgo et al. (2009). The function comorbidityMap of POGO package is able to take input an OMIM id/3 or 5 digit ICD-9-CM code of a disease or a list of gene symbols/Entrez ids and provides comorbidity map of the patient based on the relative risk and ϕ-correlation. comorbidityMap requires two parameters: id list and id type. An example and its output is given in Figure 9.

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