使用Inception Capsule网络改进蛋白质Gamma-Turn预测
Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks
摘要：
蛋白质γ转角预测在蛋白质功能研究和实验设计中具有重要意义。目前已有几种方法对伽玛转弯进行了预测，但结果都不理想，马修相关系数（MCC）在0.2-0.4之间。预测精度低的一个原因是方法的能力有限;特别是，传统的机器学习方法，如SVM可能无法很好地提取高级别的特征来区分转向或非转向。因此，探索新的机器学习方法进行预测是值得的。一种名为胶囊网络（CapsuleNet）的尖端深度神经网络为伽马转弯预测提供了新的机会。即使输入样本的数量相对较少，CapsuleNet的capsules也可以非常有效地提取分类任务的高级特征。在这里，我们提出了一个用于伽马转弯预测的深度初始胶囊网络。它在伽马转弯基准GT 320上的性能达到了0.45的MCC，大大超过了之前最好的方法，MCC为0.38。这是第一个利用深度神经网络的伽马转弯预测方法。此外，据我们所知，这是第一个利用胶囊网络的生物信息学应用，这将为社区提供一个有用的例子。
英文摘要：
Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy is the limited capacity of the methods; in particular, the traditional machine-learning methods like SVM may not extract high-level features well to distinguish between turn or non-turn. Hence, it is worthwhile exploring new machine-learning methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are very effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provides a useful example for the community.
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。
