Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 14, Issue 5, Pages 1147-1153Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2016.2607717
Keywords
Cancer stage prediction; classification; kidney cancer; network fusion; data integration
Categories
Funding
- National Science Foundation of China [61133010, 61520106006, 31571364, 61532008, 61572364, 61303111, 61411140249, 61402334, 61472280]
- China Postdoctoral Science Foundation [2015M580352, 2014M561513]
- National High-Tech RD Program (863) [2014AA021502, 2015AA020101]
- NIH [R01 GM109068, R01 MH104680, R01MH107354]
- NSF [1539067]
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In this study, in order to take advantage of complementary information from different types of data for better disease status diagnosis, we combined gene expression with DNA methylation data and generated a fused network, based on which the stages of Kidney Renal Cell Carcinoma (KIRC) can be better identified. It is well recognized that a network is important for investigating the connectivity of disease groups. We exploited the potential of the network's features to identify the KIRC stage. We first constructed a patient network from each type of data. We then built a fused network based on network fusion method. Based on the link weights of patients, we used a generalized linear model to predict the group of KIRC subjects. Finally, the group prediction method was applied to test the power of network-based features. The performance (e.g., the accuracy of identifying cancer stages) when using the fused network from two types of data is shown to be superior to that when using two patient networks from only one data type. The work provides a good example for using network based features from multiple data types for a more comprehensive diagnosis.
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