TY - JOUR
T1 - A novel algorithm based on bi-random walks to identify disease-related lncRNAs
AU - Hu, Jialu
AU - Gao, Yiqun
AU - Li, Jing
AU - Zheng, Yan
AU - Wang, Jingru
AU - Shang, Xuequn
N1 - Publisher Copyright:
© 2019 Hu et al.
PY - 2019/11/25
Y1 - 2019/11/25
N2 - Backgrounds: There is evidence to suggest that lncRNAs are associated with distinct and diverse biological processes. The dysfunction or mutation of lncRNAs are implicated in a wide range of diseases. An accurate computational model can benefit the diagnosis of diseases and help us to gain a better understanding of the molecular mechanism. Although many related algorithms have been proposed, there is still much room to improve the accuracy of the algorithm. Results: We developed a novel algorithm, BiWalkLDA, to predict disease-related lncRNAs in three real datasets, which have 528 lncRNAs, 545 diseases and 1216 interactions in total. To compare performance with other algorithms, the leave-one-out validation test was performed for BiWalkLDA and three other existing algorithms, SIMCLDA, LDAP and LRLSLDA. Additional tests were carefully designed to analyze the parameter effects such as α, β, l and r, which could help user to select the best choice of these parameters in their own application. In a case study of prostate cancer, eight out of the top-ten disease-related lncRNAs reported by BiWalkLDA were previously confirmed in literatures. Conclusions: In this paper, we develop an algorithm, BiWalkLDA, to predict lncRNA-disease association by using bi-random walks. It constructs a lncRNA-disease network by integrating interaction profile and gene ontology information. Solving cold-start problem by using neighbors' interaction profile information. Then, bi-random walks was applied to three real biological datasets. Results show that our method outperforms other algorithms in predicting lncRNA-disease association in terms of both accuracy and specificity. Availability: https://github.com/screamer/BiwalkLDA
AB - Backgrounds: There is evidence to suggest that lncRNAs are associated with distinct and diverse biological processes. The dysfunction or mutation of lncRNAs are implicated in a wide range of diseases. An accurate computational model can benefit the diagnosis of diseases and help us to gain a better understanding of the molecular mechanism. Although many related algorithms have been proposed, there is still much room to improve the accuracy of the algorithm. Results: We developed a novel algorithm, BiWalkLDA, to predict disease-related lncRNAs in three real datasets, which have 528 lncRNAs, 545 diseases and 1216 interactions in total. To compare performance with other algorithms, the leave-one-out validation test was performed for BiWalkLDA and three other existing algorithms, SIMCLDA, LDAP and LRLSLDA. Additional tests were carefully designed to analyze the parameter effects such as α, β, l and r, which could help user to select the best choice of these parameters in their own application. In a case study of prostate cancer, eight out of the top-ten disease-related lncRNAs reported by BiWalkLDA were previously confirmed in literatures. Conclusions: In this paper, we develop an algorithm, BiWalkLDA, to predict lncRNA-disease association by using bi-random walks. It constructs a lncRNA-disease network by integrating interaction profile and gene ontology information. Solving cold-start problem by using neighbors' interaction profile information. Then, bi-random walks was applied to three real biological datasets. Results show that our method outperforms other algorithms in predicting lncRNA-disease association in terms of both accuracy and specificity. Availability: https://github.com/screamer/BiwalkLDA
KW - Bi-random walks
KW - Gene ontology
KW - Interaction profile
KW - LncRNA-disease association
UR - http://www.scopus.com/inward/record.url?scp=85075532821&partnerID=8YFLogxK
U2 - 10.1186/s12859-019-3128-3
DO - 10.1186/s12859-019-3128-3
M3 - 文章
C2 - 31760932
AN - SCOPUS:85075532821
SN - 1471-2105
VL - 20
JO - BMC Bioinformatics
JF - BMC Bioinformatics
M1 - 569
ER -