计算生物学中的深度学习：进展、挑战与未来展望
Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook
AI总结：
该综述梳理了计算生物学中深度学习的发展历程、优势与挑战，聚焦其在DNA序列分析、蛋白质结构预测等领域的应用进展，并展望了该领域突破现有瓶颈、深化生物系统研究的前景。
摘要：
深度学习已成为计算生物学领域的强大工具，长期以来革新了生物数据的分析与解读。在这篇综述文章中，我们深入探讨了计算生物学中深度学习的多个方面，具体考察了其发展历史、优势与挑战，重点聚焦两大核心应用：DNA序列分类与预测，以及基于序列数据的蛋白质结构预测，此外还对该领域的发展前景提出了见解。要充分发挥深度学习在计算生物学中的潜力，应对随之而来的挑战至关重要，这些挑战包括对大型标注数据集的需求以及深度学习模型的可解释性问题。深度学习在DNA序列分析中的应用为基因组变异检测和基因表达分析带来了重大变革，极大推动了个性化医疗和药物研发的发展。已有研究表明，卷积神经网络（Convolutional neural networks, CNNs）在预测遗传变异和基因表达水平方面具有极高的准确性。深度学习技术还被用于分析表观遗传数据，包括DNA甲基化和组蛋白修饰，这为代谢疾病和基因调控研究提供了宝贵见解。蛋白质结构预测领域也受到了深度学习的显著影响，它使得精准确定蛋白质三维结构并预测其相互作用成为可能。深度学习在计算生物学中的未来前景广阔，随着先进深度学习模型和解读技术的发展，学界有望克服当前挑战，进一步深化对生物系统的理解。
英文摘要：
Deep learning has become a powerful tool in computational biology, revolutionising the analysis and interpretation of biological data over time. In our article review, we delve into various aspects of deep learning in computational biology. Specifically, we examine its history, advantages, and challenges. Our focus is on two primary applications: DNA sequence classification and prediction, as well as protein structure prediction from sequence data. Additionally, we provide insights into the outlook for this field. To fully harness the potential of deep learning in computational biology, it is crucial to address the challenges that come with it. These challenges include the requirement for large, labelled datasets and the interpretability of deep learning models. The use of deep learning in the analysis of DNA sequences has brought about a significant transformation in the detection of genomic variants and the analysis of gene expression. This has greatly contributed to the advancement of personalised medicine and drug discovery. Convolutional neural networks (CNNs) have been shown to be highly accurate in predicting genetic variations and gene expression levels. Deep learning techniques are used for analysing epigenetic data, including DNA methylation and histone modifications. This provides valuable insights into metabolic conditions and gene regulation. The field of protein structure prediction has been significantly impacted by deep learning, which has enabled accurate determination of the three-dimensional shape of proteins and prediction of their interactions. The future of deep learning in computational biology looks promising. With the development of advanced deep learning models and interpretation techniques, there is potential to overcome current challenges and further our understanding of biological systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
