| Autor: |
Yongxian Fan, Hui Xiong, Guicong Sun |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
BMC Bioinformatics, Vol 24, Iss 1, Pp 1-14 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2105 |
| DOI: |
10.1186/s12859-023-05378-x |
| Popis: |
Abstract Background Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by difficulty communicating with society and others, behavioral difficulties, and a brain that processes information differently than normal. Genetics has a strong impact on ASD associated with early onset and distinctive signs. Currently, all known ASD risk genes are able to encode proteins, and some de novo mutations disrupting protein-coding genes have been demonstrated to cause ASD. Next-generation sequencing technology enables high-throughput identification of ASD risk RNAs. However, these efforts are time-consuming and expensive, so an efficient computational model for ASD risk gene prediction is necessary. Results In this study, we propose DeepASDPerd, a predictor for ASD risk RNA based on deep learning. Firstly, we use K-mer to feature encode the RNA transcript sequences, and then fuse them with corresponding gene expression values to construct a feature matrix. After combining chi-square test and logistic regression to select the best feature subset, we input them into a binary classification prediction model constructed by convolutional neural network and long short-term memory for training and classification. The results of the tenfold cross-validation proved our method outperformed the state-of-the-art methods. Dataset and source code are available at https://github.com/Onebear-X/DeepASDPred is freely available. Conclusions Our experimental results show that DeepASDPred has outstanding performance in identifying ASD risk RNA genes. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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