7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Inferring microRNA-disease association by hybrid recommendation algorithm and unbalanced bi-random walk on heterogeneous network

      research-article
      1 , 1 , 1 , 2 ,
      Scientific Reports
      Nature Publishing Group UK

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          More and more research works have indicated that microRNAs (miRNAs) play indispensable roles in exploring the pathogenesis of diseases. Detecting miRNA-disease associations by experimental techniques in biology is expensive and time-consuming. Hence, it is important to propose reliable and accurate computational methods to exploring potential miRNAs related diseases. In our work, we develop a novel method (BRWHNHA) to uncover potential miRNAs associated with diseases based on hybrid recommendation algorithm and unbalanced bi-random walk. We first integrate the Gaussian interaction profile kernel similarity into the miRNA functional similarity network and the disease semantic similarity network. Then we calculate the transition probability matrix of bipartite network by using hybrid recommendation algorithm. Finally, we adopt unbalanced bi-random walk on the heterogeneous network to infer undiscovered miRNA-disease relationships. We tested BRWHNHA on 22 diseases based on five-fold cross-validation and achieves reliable performance with average AUC of 0.857, which an area under the ROC curve ranging from 0.807 to 0.924. As a result, BRWHNHA significantly improves the performance of inferring potential miRNA-disease association compared with previous methods. Moreover, the case studies on lung neoplasms and prostate neoplasms also illustrate that BRWHNHA is superior to previous prediction methods and is more advantageous in exploring potential miRNAs related diseases. All source codes can be downloaded from https://github.com/myl446/BRWHNHA.

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: not found

          A new method to measure the semantic similarity of GO terms.

          Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            MicroRNA-221/222 confers tamoxifen resistance in breast cancer by targeting p27Kip1.

            We explored the role of microRNAs (miRNAs) in acquiring resistance to tamoxifen, a drug successfully used to treat women with estrogen receptor-positive breast cancer. miRNA microarray analysis of MCF-7 cell lines that are either sensitive (parental) or resistant (4-hydroxytamoxifen-resistant (OHT(R))) to tamoxifen showed significant (>1.8-fold) up-regulation of eight miRNAs and marked down-regulation (>50%) of seven miRNAs in OHT(R) cells compared with parental MCF-7 cells. Increased expression of three of the most promising up-regulated (miR-221, miR-222, and miR-181) and down-regulated (miR-21, miR-342, and miR-489) miRNAs was validated by real-time reverse transcription-PCR. The expression of miR-221 and miR-222 was also significantly (2-fold) elevated in HER2/neu-positive primary human breast cancer tissues that are known to be resistant to endocrine therapy compared with HER2/neu-negative tissue samples. Ectopic expression of miR-221/222 rendered the parental MCF-7 cells resistant to tamoxifen. The protein level of the cell cycle inhibitor p27(Kip1), a known target of miR-221/222, was reduced by 50% in OHT(R) cells and by 28-50% in miR-221/222-overexpressing MCF-7 cells. Furthermore, overexpression of p27(Kip1) in the resistant OHT(R) cells caused enhanced cell death when exposed to tamoxifen. This is the first study demonstrating a relationship between miR-221/222 expression and HER2/neu overexpression in primary breast tumors that are generally resistant to tamoxifen therapy. This finding also provides the rationale for the application of altered expression of specific miRNAs as a predictive tamoxifen-resistant breast cancer marker.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              RWRMDA: predicting novel human microRNA-disease associations.

              Recently, more and more research has shown that microRNAs (miRNAs) play critical roles in the development and progression of various diseases, but it is not easy to predict potential human miRNA-disease associations from the vast amount of biological data. Computational methods for predicting potential disease-miRNA associations have gained a lot of attention based on their feasibility, guidance and effectiveness. Differing from traditional local network similarity measures, we adopted global network similarity measures and developed Random Walk with Restart for MiRNA-Disease Association (RWRMDA) to infer potential miRNA-disease interactions by implementing random walk on the miRNA-miRNA functional similarity network. We tested RWRMDA on 1616 known miRNA-disease associations based on leave-one-out cross-validation, and achieved an area under the ROC curve of 86.17%, which significantly improves previous methods. The method was also applied to three cancers for accuracy evaluation. As a result, 98% (Breast cancer), 74% (Colon cancer), and 88% (Lung cancer) of top 50 predicted miRNAs are confirmed by published experiments. These results suggest that RWRMDA will represent an important bioinformatics resource in biomedical research of both miRNAs and diseases.
                Bookmark

                Author and article information

                Contributors
                yuzuguo@aliyun.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                21 February 2019
                21 February 2019
                2019
                : 9
                : 2474
                Affiliations
                [1 ]ISNI 0000 0000 8633 7608, GRID grid.412982.4, Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, , Xiangtan University, ; Xiangtan, Hunan 411105 P.R. China
                [2 ]ISNI 0000000089150953, GRID grid.1024.7, School of Electrical Engineering and Computer Science, , Queensland University of Technology, ; Brisbane, Q4001 Australia
                Author information
                http://orcid.org/0000-0001-5913-9646
                Article
                39226
                10.1038/s41598-019-39226-x
                6385311
                30792474
                bf886672-8646-4dd6-9bb0-16e0ca0532a6
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 September 2018
                : 18 January 2019
                Funding
                Funded by: Youth Innovation Promotion Association of the Chinese Academy of Sciencesject of Hunan Province of China (Grant No. Cx2016B252).
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 11871061
                Award Recipient :
                Funded by: Collaborative Research project for Overseas Scholars (including Hong Kong and Macau) of National Natural Science Foundation of China (Grant No. 61828203); Chinese Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT)(Grant No. IRT\_15R58); Research Foundation of Education Commission of Hunan Province of China (Grant No. 17K090)
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                Uncategorized

                Comments

                Comment on this article