Identifying Associations between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism.

Research has shown that small nucleolar RNAs (snoRNAs) play crucial roles in various biological processes, and understanding disease pathogenesis by studying their relationship with diseases is beneficial. Currently, known associations are insufficient, and conventional biological experiments are costly and time-consuming. Therefore, developing efficient computational methods is crucial for identifying potential snoRNA-disease associations. In this paper, a method to identify snoRNA-disease associations based on graph convolutional network and multi-view graph attention mechanism (GCASDA) is proposed. Firstly, the similarity matrices of snoRNAs and diseases are calculated based on biological entity-related information, and the weights of the edges between the snoRNA nodes and the disease nodes are supplemented by random forest. Then two homogeneous graphs and one heterogeneous graph are constructed. Subsequently, different types of embedded features are extracted from the graphs using specific graph convolutional network structure and integrated through a multi-view graph attention mechanism to obtain node embedded feature representations. Finally, for each pair of nodes, in addition to their global features, node interaction features are passed together to a multilayer perceptron neural network (MLP) to identify snoRNA-disease associations. Experimental results show that GCASDA achieves 0.9356 and 0.9294 in AUC and AUPR, respectively, and significantly outperformed other state-of-the-art methods on the basis of different evaluation metrics. Furthermore, the case study could further demonstrate the realistic feasibility of GCASDA.

Accurate prediction and key protein sequence feature identification of cyclins.

Cyclin proteins are a group of proteins that activate the cell cycle by forming complexes with cyclin-dependent kinases. Identifying cyclins correctly can provide key clues to understanding the function of cyclins. However, due to the low similarity between cyclin protein sequences, the advancement of a machine learning-based approach to identify cycles is urgently needed. In this study, cyclin protein sequence features were extracted using the profile-based auto-cross covariance method. Then the features were ranked and selected with maximum relevance-maximum distance (MRMD) 1.0 and MRMD2.0. Finally, the prediction model was assessed through 10-fold cross-validation. The computational experiments showed that the best protein sequence features generated by MRMD1.0 could correctly predict 98.2% of cyclins using the random forest (RF) classifier, whereas seven-dimensional key protein sequence features identified with MRMD2.0 could correctly predict 96.1% of cyclins, which was superior to previous studies on the same dataset both in terms of dimensionality and performance comparisons. Therefore, our work provided a valuable tool for identifying cyclins. The model data can be downloaded from https://github.com/YUshunL/cyclin.

Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.

DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play a particular function in disease prevention and treatment. The limitation of identifying DNA-binding protein members from the sequence database is time-consuming, costly, and ineffective. Therefore, efficient methods for improving DBP classification are crucial to disease research. In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks. The method combines two feature selection methods, MonoDiKGap and Kmer, and then used MRMD2.0 to remove redundant features. According to the results, 94% of DBP were correctly recognized, and the accuracy of the independent test set reached 91.2%. This means Hybrid_ DBP can become a useful prediction tool for predicting DBP.