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2015 ; 5
(ä): 11338
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Constructing lncRNA functional similarity network based on lncRNA-disease
associations and disease semantic similarity
#MMPMID26061969
Chen X
; Yan CC
; Luo C
; Ji W
; Zhang Y
; Dai Q
Sci Rep
2015[Jun]; 5
(ä): 11338
PMID26061969
show ga
Increasing evidence has indicated that plenty of lncRNAs play important roles in
many critical biological processes. Developing powerful computational models to
construct lncRNA functional similarity network based on heterogeneous biological
datasets is one of the most important and popular topics in the fields of both
lncRNAs and complex diseases. Functional similarity network construction could
benefit the model development for both lncRNA function inference and
lncRNA-disease association identification. However, little effort has been
attempted to analysis and calculate lncRNA functional similarity on a large
scale. In this study, based on the assumption that functionally similar lncRNAs
tend to be associated with similar diseases, we developed two novel lncRNA
functional similarity calculation models (LNCSIM). LNCSIM was evaluated by
introducing similarity scores into the model of Laplacian Regularized Least
Squares for LncRNA-Disease Association (LRLSLDA) for lncRNA-disease association
prediction. As a result, new predictive models improved the performance of
LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease
associations datasets. Furthermore, some of the predictive results for colorectal
cancer and lung cancer were verified by independent biological experimental
studies. It is anticipated that LNCSIM could be a useful and important biological
tool for human disease diagnosis, treatment, and prevention.