Proteins play a pivotal role in biological systems. The use of machine learning algorithms for protein classification can assist and even guide biological experiments, offering crucial insights for biotechnological applications. We propose a support bio-sequence machine for proteins, a model specifically designed for biological sequence classification. This model starts with raw sequences and groups amino acids based on their physicochemical properties. It incorporates sequence alignment to measure the similarities between proteins and uses a novel MKL approach to integrate various types of information, utilizing support vector machines for classification prediction. The results indicate that our model demonstrates commendable performance across 10 datasets in terms of the identification of protein function and posttranslational modification. This research not only showcases state-of-the-art work in protein classification but also paves the way for new directions in this domain, representing a beneficial endeavour in the development of platforms tailored for biological sequence classification. SBSM-Pro is available for access at http://lab.malab.cn/soft/SBSM-Pro/.

我们提出了一种支持生物序列机器用于蛋白质分类的模型，该模型通过使用机器学习算法来帮助和引导生物实验，在生物技术应用中提供了重要的洞察。该研究展示了在蛋白质分类领域的最新工作，并为生物序列分类领域的新方向铺平了道路，对于定制的平台开发具有积极意义。

SBSM-Pro：支持蛋白质的生物序列机器