Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features.

MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms. The work presents a variational gated autoencoder-based feature extraction model to extract complex contextual features for inferring potential disease-miRNA associations. Specifically, our model fuses three different similarities of miRNAs into a comprehensive miRNA network and then combines two various similarities of diseases into a comprehensive disease network, respectively. Then, a novel graph autoencoder is designed to extract multilevel representations based on variational gate mechanisms from heterogeneous networks of miRNAs and diseases. Finally, a gate-based association predictor is devised to combine multiscale representations of miRNAs and diseases via a novel contrastive cross-entropy function, and then infer disease-miRNA associations. Experimental results indicate that our proposed model achieves remarkable association prediction performance, proving the efficacy of the variational gate mechanism and contrastive cross-entropy loss for inferring disease-miRNA associations.

Context-aware dynamic neural computational models for accurate Poly(A) signal prediction.

Accurately predicting Polyadenylation (Poly(A)) signals isthe key to understand the mechanism of translation regulation and mRNA metabolism. However, existing computational algorithms fail to work well for predicting Poly(A) signals due to the vanishing gradient problem when simply increasing the number of layers. In this work, we devise a spatiotemporal context-aware neural model called ACNet for Poly(A) signal prediction based on co-occurrence embedding. Specifically, genomic sequences of Poly(A) signals are first split into k-mer sequences, and k-mer embeddings are pre-trained based on the co-occurrence matrix information; Then, gated residual networks are devised to fully extract spatial information, which has an excellent ability to control the information flow and ease the problem of vanishing gradients. The gated mechanism generates channel weights by a dilated convolution and aggregates local features by identity connections which are obtained by multi-scale dilated convolutions. Experimental results indicate that our ACNet model outperforms the state-of-the-art prediction methods on various Poly(A) signal data, and an ablation study shows the effectiveness of the design strategy.

Context-Aware Poly(A) Signal Prediction Model via Deep Spatial-Temporal Neural Networks.

Polyadenylation Poly(A) is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial-temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.

Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix.

Apoptosis proteins are strongly related to many diseases and play an indispensable role in maintaining the dynamic balance between cell death and division in vivo. Obtaining localization information on apoptosis proteins is necessary in understanding their function. To date, few researchers have focused on the problem of apoptosis data imbalance before classification, while this data imbalance is prone to misclassification. Therefore, in this work, we introduce a method to resolve this problem and to enhance prediction accuracy. Firstly, the features of the protein sequence are captured by combining Improving Pseudo-Position-Specific Scoring Matrix (IM-Psepssm) with the Bidirectional Correlation Coefficient (Bid-CC) algorithm from position-specific scoring matrix. Secondly, different features of fusion and resampling strategies are used to reduce the impact of imbalance on apoptosis protein datasets. Finally, the eigenvector adopts the Support Vector Machine (SVM) to the training classification model, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results indicate that, under the same feature vector, adopting resampling methods remarkably boosts many significant indicators in the unsampling method for predicting the localization of apoptosis proteins in the ZD98, ZW225, and CL317 databases. Additionally, we also present new user-friendly local software for readers to apply; the codes and software can be freely accessed at https://github.com/ruanxiaoli/Im-Psepssm.

DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction.

BACKGROUND: Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it's very urgent to develop efficient computational approaches for predicting PSS based on sequence information alone. Moreover, the feature matrix of a protein contains two dimensions: the amino-acid residue dimension and the feature vector dimension. Existing deep learning based methods have achieved remarkable performances of PSS prediction, but the methods often utilize the features from the amino-acid dimension. Thus, there is still room to improve computational methods of PSS prediction. RESULTS: We propose a novel deep neural network method, called DeepACLSTM, to predict 8-category PSS from protein sequence features and profile features. Our method efficiently applies asymmetric convolutional neural networks (ACNNs) combined with bidirectional long short-term memory (BLSTM) neural networks to predict PSS, leveraging the feature vector dimension of the protein feature matrix. In DeepACLSTM, the ACNNs extract the complex local contexts of amino-acids; the BLSTM neural networks capture the long-distance interdependencies between amino-acids. Furthermore, the prediction module predicts the category of each amino-acid residue based on both local contexts and long-distance interdependencies. To evaluate performances of DeepACLSTM, we conduct experiments on three publicly available datasets: CB513, CASP10 and CASP12. Results indicate that the performance of our method is superior to the state-of-the-art baselines on three publicly datasets. CONCLUSIONS: Experiments demonstrate that DeepACLSTM is an efficient predication method for predicting 8-category PSS and has the ability to extract more complex sequence-structure relationships between amino-acid residues. Moreover, experiments also indicate the feature vector dimension contains the useful information for improving PSS prediction.

Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks.

Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature vector dimension. Common approaches to predict secondary structures only focus on the amino-acid residue dimension. However, the feature vector dimension may also contain useful information for PSSP. To integrate the information on both dimensions of the matrix, we propose a hybrid deep learning framework, two-dimensional convolutional bidirectional recurrent neural network (2C-BRNN), for improving the accuracy of 8-class secondary structure prediction. The proposed hybrid framework is to extract the discriminative local interactions between amino-acid residues by two-dimensional convolutional neural networks (2DCNNs), and then further capture long-range interactions between amino-acid residues by bidirectional gated recurrent units (BGRUs) or bidirectional long short-term memory (BLSTM). Specifically, our proposed 2C-BRNNs framework consists of four models: 2DConv-BGRUs, 2DCNN-BGRUs, 2DConv-BLSTM and 2DCNN-BLSTM. Among these four models, the 2DConv- models only contain two-dimensional (2D) convolution operations. Moreover, the 2DCNN- models contain 2D convolutional and pooling operations. Experiments are conducted on four public datasets. The experimental results show that our proposed 2DConv-BLSTM model performs significantly better than the benchmark models. Furthermore, the experiments also demonstrate that the proposed models can extract more meaningful features from the matrix of proteins, and the feature vector dimension is also useful for PSSP. The codes and datasets of our proposed methods are available at https://github.com/guoyanb/JBCB2018/ .