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• W3137447770 abstract "Background: Protein secondary structure is vital to predicting the tertiary structure, which is essential in deciding protein function and drug designing. Therefore, there is a high requirement of computational methods to predict secondary structure from their primary sequence. Protein primary sequences represented as a linear combination of twenty amino acid characters and contain the contextual information for secondary structure prediction. Objective and Methods: Protein secondary structure predicted from their primary sequences using a deep recurrent neural network. Protein secondary structure depends on local and long-range residues in primary sequences. In the proposed work, the local contextual information of amino acid residues captures with character n-gram. A dense embedding vector represents this local contextual information. Furthermore, the bidirectional long short-term memory (Bi-LSTM) model is used to capture the long-range contexts by extracting the past and future residues information in primary sequences. Results: The proposed deep recurrent architecture is evaluated for its efficacy for datasets, namely ss.txt, RS126, and CASP9. The model shows the Q3 accuracies of 88.45%, 83.48%, and 86.69% for ss.txt, RS126, and CASP9, respectively. The performance of the proposed model is also compared with other state-of-the-art methods available in the literature. Conclusion: After a comparative analysis, it was observed that the proposed model is performing better in comparison to state-of-art methods." @default.
• W3137447770 created "2021-03-29" @default.
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• W3137447770 date "2021-02-01" @default.
• W3137447770 modified "2023-09-27" @default.
• W3137447770 title "Protein Secondary Structure Prediction Using Character Bi-gram Embedding and Bi-LSTM" @default.
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• W3137447770 doi "https://doi.org/10.2174/1574893615999200601122840" @default.
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