WSHNN: A Weakly Supervised Hybrid Neural Network for the Identification of DNA-Protein Binding Sites.
Curr Comput Aided Drug Des
; 2024 Feb 12.
Article
em En
| MEDLINE
| ID: mdl-38347788
ABSTRACT
INTRODUCTION:
Transcription factors are vital biological components that control gene expression, and their primary biological function is to recognize DNA sequences. As related research continues, it was found that the specificity of DNA-protein binding has a significant role in gene expression, regulation, and especially gene therapy. Convolutional Neural Networks (CNNs) have become increasingly popular for predicting DNa-protein-specific binding sites, but their accuracy in prediction needs to be improved.METHODS:
We proposed a framework for combining multi-Instance Learning (MIL) and a hybrid neural network named WSHNN. First, we utilized sliding windows to split the DNA sequences into multiple overlapping instances, each instance containing multiple bags. Then, the instances were encoded using a K-mer encoding. Afterward, the scores of all instances in the same bag were calculated separately by a hybrid neural network.RESULTS:
Finally, a fully connected network was utilized as the final prediction for that bag. The framework could achieve the performances of 90.73% in Pre, 82.77% in Recall, 87.17% in Acc, 0.8657 in F1-score, and 0.7462 in MCC, respectively. In addition, we discussed the performance of K-mer encoding. Compared with other art-of-the-state efforts, the model has better performance with sequence information.CONCLUSION:
From the experimental results, it can be concluded that Bi-directional Long-ShortTerm Memory (Bi-LSTM) can better capture the long-sequence relationships between DNA sequences (the code and data can be visited at https//github.com/baowz12345/Weak_ Super_Network).
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Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Curr Comput Aided Drug Des
Assunto da revista:
FARMACOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China