Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression

Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression

Abstract Background In human genomes, long non-coding RNAs (lncRNAs) have attracted more and more attention because their dysfunctions are involved in many diseases. However, the associations between lncRNAs and diseases (LDA) still remain unknown in most cases. While identifying disease-related lnc...

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Bibliographic Details
Main Authors: Jian-Yu Shi, Hua Huang, Yan-Ning Zhang, Yu-Xi Long, Siu-Ming Yiu
Format: Article
Language:English
Published: BMC 2017-12-01
Series:BMC Medical Genomics
Subjects:
lncRNA-disease association
Graph regression
Prediction
Discrete
Continued
Sequence feature
Online Access:http://link.springer.com/article/10.1186/s12920-017-0305-y

Internet

http://link.springer.com/article/10.1186/s12920-017-0305-y

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