Protein secondary structure prediction.

<abstract> <p>As an important task in bioinformatics, protein secondary structure prediction (PSSP) is not only beneficial to protein function research and tertiary structure prediction, but also to promote the design and development of new drugs. However, current PSSP methods cannot sufficiently extract effective features. In this study, we propose a novel deep learning model WGACSTCN, which combines Wasserstein generative adversarial network with gradient penalty (WGAN-GP), convolutional block attention module (CBAM) and temporal convolutional network (TCN) for 3-state and 8-state PSSP. In the proposed model, the mutual game of generator and discriminator in WGAN-GP module can effectively extract protein features, and our CBAM-TCN local extraction module can capture key deep local interactions in protein sequences segmented by sliding window technique, and the CBAM-TCN long-range extraction module can further capture the key deep long-range interactions in sequences. We evaluate the performance of the proposed model on seven benchmark datasets. Experimental results show that our model exhibits better prediction performance compared to the four state-of-the-art models. The proposed model has strong feature extraction ability, which can extract important information more comprehensively.</p> </abstract>

In recent years, lung cancer has become one of the most lethal factors to human beings. Clinical data show that the probability of lung nodules developed into lung cancer is about 30%. Due to the lack of obvious symptoms, around 70% of lung cancer patients in China are in advanced stage of lung cancer when firstly diagnosed. Therefore, early identification of lung nodules is of great significance for early diagnosis and therapy. Currently, artificial intelligence has been widely used to generate predictive model of lung nodules by learning algorithms adapted to image characteristics, leading to improved accuracy and higher sensitivity of diagnosis of early lung cancer. In this work, Luna16 (lung nodule analysis 2016, containing a total of 888 low-dose chest Computed Tomography (CT) thin-slice plain scan lesions) were selected as the data set, providing a total of 1018 CT slices with the most representative shape of lung nodules in this analysis. Next, this project was performed on Baidu AI Studio platform, applying both U-Net and PSP Net to train a model of rapid detection of lung nodules. The training process generated a model providing a rapid and accurate identification of lung nodules larger than 3 mm in diameter. Results showed that the accuracy of U-Net was higher than that of PSP Net, indicating a high potential in further clinical diagnosis in lung cancer.

Protein secondary structure prediction (PSSP) is a challenging task in computational biology. However, existing models with deep architectures are not sufficient and comprehensive for deep long-range feature extraction of long sequences. This paper proposes a novel deep learning model to improve Protein secondary structure prediction. In the model, our proposed bidirectional temporal convolutional network (BTCN) can extract the bidirectional deep local dependencies in protein sequences segmented by the sliding window technique, the bidirectional long short-term memory (BLSTM) network can extract the global interactions between residues, and our proposed multi-scale bidirectional temporal convolutional network (MSBTCN) can further capture the bidirectional multi-scale long-range features of residues while preserving the hidden layer information more comprehensively. In particular, we also propose that fusing the features of 3-state and 8-state Protein secondary structure prediction can further improve the prediction accuracy. Moreover, we also propose and compare multiple novel deep models by combining bidirectional long short-term memory with temporal convolutional network (TCN), reverse temporal convolutional network (RTCN), multi-scale temporal convolutional network (multi-scale bidirectional temporal convolutional network), bidirectional temporal convolutional network and multi-scale bidirectional temporal convolutional network, respectively. Furthermore, we demonstrate that the reverse prediction of secondary structure outperforms the forward prediction, suggesting that amino acids at later positions have a greater impact on secondary structure recognition. Experimental results on benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14, and CB513 show that our methods achieve better prediction performance compared to five state-of-the-art methods.