Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature
| Autor: | Zhen Shen, De-Shuang Huang, Su-Ping Deng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Applied Mathematics 0206 medical engineering Computational Biology RNA-Binding Proteins RNA Genomics Pattern recognition 02 engineering and technology Field (computer science) Convolution Kernel (image processing) Encoding (memory) Genetics Feature (machine learning) Neural Networks Computer Artificial intelligence Layer (object-oriented design) business Algorithms 020602 bioinformatics Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17:1483-1492 |
| ISSN: | 2374-0043 1545-5963 |
| Popis: | RNA-Protein binding is involved in many different biological processes. With the progress of technology, more and more data are available for research. Based on these data, many prediction methods have been proposed to predict RNA-Protein binding preference. Some of these methods use only RNA sequence features for prediction, and some methods use multiple features for prediction. But, the performance of these methods is not satisfactory. In this study, we propose an improved capsule network to predict RNA-protein binding preferences, which can use both RNA sequence features and structure features. Experimental results show that our proposed method iCapsule performs better than three baseline methods in this field. We used both RNA sequence features and structure features in the model, so we tested the effect of primary capsule layer changes on model performance. In addition, we also studied the impact of model structure on model performance by performing our proposed method with different number of convolution layers and different kernel sizes. |
| Databáze: | OpenAIRE |
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