ncDLRES: a novel method for non-coding RNAs family prediction based on dynamic LSTM and ResNet
| Autor: | Yuanning Liu, Xiao-dan Zhong, Shuo Wang, Linyu Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
QH301-705.5 Applied Mathematics Research Computer applications to medicine. Medical informatics R858-859.7 Computational biology ncDLRES Non-coding RNA Biochemistry Homologous Sequences Residual neural network Calculation methods Computer Science Applications ResNet Structural Biology DNA microarray Biology (General) LSTM Molecular Biology Protein secondary structure Sequence (medicine) Coding (social sciences) ncRNAs family |
| Zdroj: | BMC Bioinformatics BMC Bioinformatics, Vol 22, Iss 1, Pp 1-14 (2021) |
| ISSN: | 1471-2105 |
| Popis: | Background Studies have proven that the same family of non-coding RNAs (ncRNAs) have similar functions, so predicting the ncRNAs family is helpful to the research of ncRNAs functions. The existing calculation methods mainly fall into two categories: the first type is to predict ncRNAs family by learning the features of sequence or secondary structure, and the other type is to predict ncRNAs family by the alignment among homologs sequences. In the first type, some methods predict ncRNAs family by learning predicted secondary structure features. The inaccuracy of predicted secondary structure may cause the low accuracy of those methods. Different from that, ncRFP directly learning the features of ncRNA sequences to predict ncRNAs family. Although ncRFP simplifies the prediction process and improves the performance, there is room for improvement in ncRFP performance due to the incomplete features of its input data. In the secondary type, the homologous sequence alignment method can achieve the highest performance at present. However, due to the need for consensus secondary structure annotation of ncRNA sequences, and the helplessness for modeling pseudoknots, the use of the method is limited. Results In this paper, a novel method “ncDLRES”, which according to learning the sequence features, is proposed to predict the family of ncRNAs based on Dynamic LSTM (Long Short-term Memory) and ResNet (Residual Neural Network). Conclusions ncDLRES extracts the features of ncRNA sequences based on Dynamic LSTM and then classifies them by ResNet. Compared with the homologous sequence alignment method, ncDLRES reduces the data requirement and expands the application scope. By comparing with the first type of methods, the performance of ncDLRES is greatly improved. |
| Databáze: | OpenAIRE |
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