Mansoor Musadaq, Nauman Mohammad, Rehman Hafeez Ur, Omar Maryam
National University of Computer and Emerging Sciences, Islamabad, Peshawar, KPK, Pakistan.
PeerJ Comput Sci. 2022 Aug 15;8:e1014. doi: 10.7717/peerj-cs.1014. eCollection 2022.
Proteins are the core of all functions pertaining to living things. They consist of an extended amino acid chain folding into a three-dimensional shape that dictates their behavior. Currently, convolutional neural networks (CNNs) have been pivotal in predicting protein functions based on protein sequences. While it is a technology crucial to the niche, the computation cost and translational invariance associated with CNN make it impossible to detect spatial hierarchies between complex and simpler objects. Therefore, this research utilizes capsule networks to capture spatial information as opposed to CNNs. Since capsule networks focus on hierarchical links, they have a lot of potential for solving structural biology challenges. In comparison to the standard CNNs, our results exhibit an improvement in accuracy. Gene Ontology Capsule GAN (GOCAPGAN) achieved an F1 score of 82.6%, a precision score of 90.4% and recall score of 76.1%.
蛋白质是所有与生物相关功能的核心。它们由一条延伸的氨基酸链折叠成三维形状，这种形状决定了它们的行为。目前，卷积神经网络（CNN）在基于蛋白质序列预测蛋白质功能方面一直起着关键作用。虽然这是一项对该领域至关重要的技术，但与CNN相关的计算成本和平移不变性使得它无法检测复杂物体和简单物体之间的空间层次结构。因此，本研究利用胶囊网络来捕获空间信息，与CNN形成对比。由于胶囊网络专注于层次链接，它们在解决结构生物学挑战方面有很大潜力。与标准CNN相比，我们的结果在准确性上有了提高。基因本体胶囊生成对抗网络（GOCAPGAN）的F1分数达到82.6%，精确率分数达到90.4%，召回率分数达到76.1%。
