Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/220135
Title: Identifying Stages of Kidney Renal Cell Carcinoma by Combining Gene Expression and DNA Methylation Data
Authors: Su-Ping Deng;Shaolong Cao;De-Shuang Huang;Yu-Ping Wang
Year: 2017
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/220135
volume: 14
issue: 5
More Information: 1147,
1153
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7563822.pdf363.25 kBAdobe PDF
Title: Identifying Stages of Kidney Renal Cell Carcinoma by Combining Gene Expression and DNA Methylation Data
Authors: Su-Ping Deng;Shaolong Cao;De-Shuang Huang;Yu-Ping Wang
Year: 2017
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/220135
volume: 14
issue: 5
More Information: 1147,
1153
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7563822.pdf363.25 kBAdobe PDF
Title: Identifying Stages of Kidney Renal Cell Carcinoma by Combining Gene Expression and DNA Methylation Data
Authors: Su-Ping Deng;Shaolong Cao;De-Shuang Huang;Yu-Ping Wang
Year: 2017
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/220135
volume: 14
issue: 5
More Information: 1147,
1153
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7563822.pdf363.25 kBAdobe PDF