Abstract
The term comorbidity refers to the coexistence of multiple diseases or disorders along with a primary disease in a patient. Hence, the prediction of disease comorbidity can identify the comorbid diseases when dealing with a primary disease. Unfortunately, since the records of comorbidity in clinic are far from complete, we can’t get enough knowledge to understand the reason for comorbidity. Though many researches have been done to predict disease comorbidity, the accuracy of these algorithms need to be improved. By investigating the factors underlying disease comorbidity, we found that a number of comorbidities are caused by common modules comprised by proteins. Thus, we here propose a novel algorithm to identify disease comorbidity by integrating different types of datasets ranging from properties to functions of protein. Results on real data of comorbidity display that our algorithm can perform better than previous approaches, and some of our new predictions are reported in literature, which can prove the effectiveness of our algorithm, and help deeply explain the molecular mechanism of disease comorbidity.
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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61520106006, 31571364, 61532008, 61572364, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61572447, and 61373098, China Postdoctoral Science Foundation Grant, Nos. 2014M561513 and 2015M580352.
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He, F., Li, N. (2016). ICPFP: A Novel Algorithm for Identification of Comorbidity Based on Properties and Functions of Protein. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_78
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DOI: https://doi.org/10.1007/978-3-319-42297-8_78
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