Membrane proteins are key gates that control various vital cellular functions. Membrane proteins are often detected using transmembrane topology prediction tools. While transmembrane topology prediction tools can detect integral membrane proteins, they do not address surface-bound proteins. In this study, we focused on finding the best techniques for distinguishing all types of membrane proteins. This research first demonstrates the shortcomings of merely using transmembrane topology prediction tools to detect all types of membrane proteins. Then, the performance of various feature extraction techniques in combination with different machine learning algorithms was explored. The experimental results obtained by cross-validation and independent testing suggest that applying an integrative approach that combines the results of transmembrane topology prediction and position-specific scoring matrix (Pse-PSSM) optimized evidence-theoretic k nearest neighbor (OET-KNN) predictors yields the best performance. The integrative approach outperforms the state-of-the-art methods in terms of accuracy and MCC, where the accuracy reached a 92.51% in independent testing, compared to the 89.53% and 79.42% accuracies achieved by the state-of-the-art methods.

中文翻译：

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检测膜蛋白的综合方法

膜蛋白是控制各种重要细胞功能的关键门。通常使用跨膜拓扑预测工具检测膜蛋白。尽管跨膜拓扑预测工具可以检测完整的膜蛋白，但不能处理表面结合的蛋白。在这项研究中，我们专注于发现区分所有类型的膜蛋白的最佳技术。这项研究首先证明了仅使用跨膜拓扑预测工具来检测所有类型的膜蛋白的缺点。然后，探索了结合不同机器学习算法的各种特征提取技术的性能。通过交叉验证和独立测试获得的实验结果表明，采用结合跨膜拓扑预测结果和特定位置评分矩阵（Pse-PSSM）优化证据理论k最近邻（OET-KNN）预测因子的方法最好的表现。集成方法在准确性和MCC方面优于最新方法，其中独立测试的准确性达到92.51％，而最新技术的准确性达到89.53％和79.42％方法。