Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of experimental limitations), reliable prediction of protein function through computational means has become crucial. This paper reviews the machine learning techniques used in the literature, following their evolution from simple algorithms such as logistic regression to more advanced methods like support vector machines and modern deep neural networks. Hyperparameter optimization methods adopted to boost prediction performance are presented. In parallel, the metamorphosis in the features used by these algorithms from classical physicochemical properties and amino acid composition, up to text-derived features from biomedical literature and learned feature representations using autoencoders, together with feature selection and dimensionality reduction techniques, are also reviewed. The success stories in the application of these techniques to both general and specific protein function prediction are discussed.

中文翻译：

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蛋白质功能预测的机器学习技术。

蛋白质在生物体中起重要作用，其功能与其结构直接相关。由于发现的蛋白质数量与其功能表征之间的差距越来越大（特别是由于实验局限性），通过计算手段可靠地预测蛋白质功能就变得至关重要。本文回顾了文献中使用的机器学习技术，它们是从简单的算法（例如逻辑回归）演变成更高级的方法（例如支持向量机和现代深度神经网络）之后演变而来的。提出了用于提高预测性能的超参数优化方法。同时，这些算法使用的特征从经典的物理化学性质和氨基酸组成中发生了变态，还审查了生物医学文献中的文本衍生特征以及使用自动编码器学习到的特征表示，以及特征选择和降维技术。讨论了将这些技术应用于一般和特定蛋白质功能预测的成功案例。