A Review of Deep Learning Techniques for Protein Function Prediction
원문 초록
Deep Learning and big data have shown tremendous success in bioinformatics and computational biology in recent years; artificial intelligence methods have also significantly contributed in the task of protein function classification. This review paper analyzes the recent developments in approaches for the task of predicting protein function using deep learning. We explain the importance of determining the protein function and why automating the following task is crucial. Then, after reviewing the widely used deep learning techniques for this task, we continue our review and highlight the emergence of the modern State of The Art (SOTA) deep learning models which have achieved groundbreaking results in the field of computer vision, natural language processing and multi-modal learning in the last few years. We hope that this review will provide a broad view of the current role and advances of deep learning in biological sciences, especially in predicting protein function tasks and encourage new researchers to contribute to this area.
함께 살펴볼 논문
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AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation
evoBPE: Evolutionary Protein Sequence Tokenization
ProtBoost: protein function prediction with Py-Boost and Graph Neural Networks -- CAFA5 top2 solution
SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction
DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
토론
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