基因是生物体蛋白质合成的基本单位,准确识别基因的翻译起始位点(TIS)对于理解调控至关重要,转录,和基因的翻译过程。然而,现有模型不能充分提取TIS序列中的特征信息,它们也不能充分捕获特征之间复杂的层次关系。因此,本文提出了一种新的预测因子CapsNet-TIS。CapsNet-TIS首先使用四种编码方法完全提取TIS序列信息,包括One-hot编码,物理结构属性(PSP)编码,核苷酸化学性质(NCP)编码,和核苷酸密度(ND)编码。接下来,利用多尺度卷积神经网络对编码特征进行特征融合,增强特征表示的全面性。最后,融合后的特征采用胶囊网络作为分类模型的主网络进行分类,以获取特征之间复杂的层次关系。此外,我们通过引入残差块来改进胶囊网络,注意频道,和BiLSTM,增强模型的特征提取和序列数据建模能力。在本文中,使用来自四个物种的TIS数据集评估CapsNet-TIS的性能:人类,鼠标,牛,果蝇,并且通过进行消融实验来证明每个部分的有效性。通过将实验结果与其他研究人员提出的模型进行比较,结果表明CapsNet-TIS具有优越的性能。
Genes are the basic units of protein synthesis in organisms, and accurately identifying the translation initiation site (TIS) of genes is crucial for understanding the regulation, transcription, and translation processes of genes. However, the existing models cannot adequately extract the feature information in TIS sequences, and they also inadequately capture the complex hierarchical relationships among features. Therefore, a novel predictor named CapsNet-TIS is proposed in this paper. CapsNet-TIS first fully extracts the TIS sequence information using four encoding methods, including One-hot encoding, physical structure property (PSP) encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Next, multi-scale convolutional neural networks are used to perform feature fusion of the encoded features to enhance the comprehensiveness of the feature representation. Finally, the fused features are classified using capsule network as the main network of the classification model to capture the complex hierarchical relationships among the features. Moreover, we improve the capsule network by introducing residual block, channel attention, and BiLSTM to enhance the model\'s feature extraction and sequence data modeling capabilities. In this paper, the performance of CapsNet-TIS is evaluated using TIS datasets from four species: human, mouse, bovine, and fruit fly, and the effectiveness of each part is demonstrated by performing ablation experiments. By comparing the experimental results with models proposed by other researchers, the results demonstrate the superior performance of CapsNet-TIS.