论文标题
使用神经网络预测二级蛋白质结构
Secondary Protein Structure Prediction Using Neural Networks
论文作者
论文摘要
在本文中，我们尝试使用神经网络结构从其主要结构（氨基酸序列）预测蛋白质的二级结构（α螺旋位置）。我们使用该FCNN实施了完全连接的神经网络（FCNN）和三个实验。首先，我们对在鼠标和人类数据集上训练和测试的模型进行了跨物种的比较。其次，我们测试了改变蛋白质序列长度的影响，我们输入了模型。第三，我们比较旨在专注于输入窗口中心的自定义错误功能。在论文结尾处，我们提出了一个可以应用于该问题的替代性，经常性的神经网络模型。
In this paper we experiment with using neural network structures to predict a protein's secondary structure (α helix positions) from only its primary structure (amino acid sequence). We implement a fully connected neural network (FCNN) and preform three experiments using that FCNN. Firstly, we do a cross-species comparison of models trained and tested on mouse and human datasets. Secondly, we test the impact of varying the length of protein sequence we input into the model. Thirdly, we compare custom error functions designed to focus on the center of the input window. At the end of paper we propose a alternative, recurrent neural network model which can be applied to the problem.
