Deep learning for mining protein data.
Brief Bioinform
; 22(1): 194-218, 2021 01 18.
Article
em En
| MEDLINE
| ID: mdl-31867611
ABSTRACT
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise de Sequência de Proteína
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Mineração de Dados
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Aprendizado Profundo
Limite:
Animals
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Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article