论文标题
对蛋白质功能预测深度学习技术的综述
A Review of Deep Learning Techniques for Protein Function Prediction
论文作者
论文摘要
近年来，深度学习和大数据在生物信息学和计算生物学方面取得了巨大的成功。人工智能方法也对蛋白质功能分类的任务做出了重大贡献。这篇评论论文分析了使用深度学习预测蛋白质功能的方法的最新进展。我们解释了确定蛋白质功能的重要性，以及为什么自动执行以下任务至关重要。然后，在回顾了这项任务的广泛使用的深度学习技术之后，我们将继续进行审查，并强调现代艺术状态（SOTA）深度学习模型的出现，这些模型在过去几年中在计算机视觉，自然语言处理和多模式学习领域中取得了突破性的发展。我们希望这篇综述将对生物科学中深度学习的当前作用和进步提供广泛的看法，尤其是在预测蛋白质功能任务并鼓励新研究人员为这一领域做出贡献时。
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.
