ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion
| Autor: | Jin Liu, Ximin Wu, Yi-Ping Phoebe Chen, Jianxin Wang, Baoshan Chen, Qingfeng Chen, Wei Lan, Dehuan Lai |
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| Rok vydání: | 2021 |
| Předmět: |
Support Vector Machine
Computer science Applied Mathematics Computational Biology Disease Computational biology Similarity measure Semantics Cross-validation Long non-coding RNA Support vector machine Similarity (network science) Mutation (genetic algorithm) Genetics Humans Genetic Predisposition to Disease RNA Long Noncoding Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18:1106-1112 |
| ISSN: | 2374-0043 1545-5963 |
| Popis: | The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods have been proposed to predict the potential lncRNA-disease relationships. However, most of them may give rise to incorrect results due to relying on single similarity measure. This article proposes a novel framework (ILDMSF) by fusing the lncRNA similarities and disease similarities, which are measured by lncRNA-related gene and known lncRNA-disease interaction and disease semantic interaction, and known lncRNA-disease interaction, respectively. Further, the support vector machine is employed to identify the potential lncRNA-disease associations based on the integrated similarity. The leave-one-out cross validation is performed to compare ILDMSF with other state of the art methods. The experimental results demonstrate our method is prospective in exploring potential correlations between lncRNA and disease. |
| Databáze: | OpenAIRE |
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