BRWSP: Predicting circRNA‐Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

Complexity - Tập 2019 Số 1 - 2019
Xiujuan Lei1, Wenxiang Zhang1
1School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, 710119, China

Tóm tắt

The circular RNAs (circRNAs) have significant effects on a variety of biological processes, the dysfunction of which is closely related to the emergence and development of diseases. Therefore, identification of circRNA‐disease associations will contribute to analysing the pathogenesis of diseases. Here, we present a computational model called BRWSP to predict circRNA‐disease associations, which searches paths on a multiple heterogeneous network based on biased random walk. Firstly, BRWSP constructs a multiple heterogeneous network by using circRNAs, diseases, and genes. Then, the biased random walk algorithm runs on the multiple heterogeneous network to search paths between circRNAs and diseases. Finally, the performance of BRWSP is significantly better than the state‐of‐the‐art algorithms. Furthermore, BRWSP further contributes to the discovery of novel circRNA‐disease associations.

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Tài liệu tham khảo

10.1073/pnas.73.11.3852

10.1073/pnas.87.19.7628

10.1002/1873-3468.12500

10.1016/j.yjmcc.2016.07.007

10.1038/nature11928

10.1093/nar/gkr1009

10.1093/database/bay044

10.1261/rna.043687.113

10.1093/nar/gkx891

10.1016/j.molp.2017.03.003

10.1016/j.celrep.2019.02.078

10.1093/nar/gkx863

10.1038/nature11993

10.1080/15476286.2015.1128065

10.1007/BF03401761

10.26599/BDMA.2019.9020005

10.26599/BDMA.2018.9020009

10.1073/pnas.0701361104

10.1155/2018/7536731

10.1155/2018/4210160

10.20892/j.issn.2095-3941.2018.0425

10.1080/15476286.2017.1279788

10.1016/j.molcel.2014.08.019

10.3390/ijms19020480

10.12659/MSM.910180

10.3389/fnmol.2018.00225

10.1158/0008-5472.CAN-18-0532

10.1093/bioinformatics/btt426

10.1093/bib/bbx130

10.1371/journal.pcbi.1005912

10.1038/s41419-018-0503-3

10.3390/ijms19113410

10.7150/ijbs.28260

10.1109/jbhi.2019.2891779

10.1109/tnb.2019.2922214

Wei H., 2019, iCircDA-MF: Identification of circRNA-disease associations based on matrix factorization, Briefings in Bioinformatics

10.1186/s12859-018-2522-6

10.1186/s13321-016-0128-4

10.1371/journal.pcbi.1005455

10.3389/fgene.2018.00411

10.3389/fmicb.2017.00233

GroverA.andLeskovecJ. node2vec: scalable feature learning for networks Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD’16 August 2016 San Francisco CA USA 855–864.

10.1093/nar/gku1011

10.1093/bioinformatics/btu684

10.1093/bioinformatics/btm087

10.1016/j.ymeth.2014.11.020

10.1093/bioinformatics/bty859

10.1093/nar/gkw943

10.1093/bioinformatics/btq064

10.1186/1471-2164-13-s7-s27

10.1186/1755-8794-8-s3-s2

10.1039/c4mb00478g

10.1093/bioinformatics/btz254

10.1093/bioinformatics/btr500

10.1093/bioinformatics/bty637

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Complexity

Tập 2019 Số 1

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