BackgroundStudies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods.ResultsIn this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions.ConclusionsOur KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.
Given the similarity of handwritten formula symbols and various handwriting styles, this paper proposes a squeeze-extracted multi-feature convolution neural network (SE-MCNN) to improve the recognition rate of handwritten formula symbols. The system proposed in this paper integrates the eightdirectional feature of the original sequence in the convolutional layer, which significantly compensates for the lost dynamic trajectory information in the handwritten formula symbol. Meanwhile, the joint loss is constructed to improve the discriminability of features in the way of supervised learning, which enlarges the inter-class difference and decreases inner-class similarity. The standard mathematical formula symbol library provided by the Competition Organization on Recognition of Online Handwritten Mathematical Expression (CROHME) is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed SE-MCNN approach outperforms the state-of-the-art methods even at the condition of without using the data augmentation. INDEX TERMS Artificial neural network, multi-feature, joint training, discriminative feature.
For GMM-UBM based text-independent speaker recognition, the performance decreases significantly when the utterance is getting too short, and that is mostly due to the lack of distinguishable information from a single kind of feature. Fusion of different features followed by a dimensionality reduction process has been proved useful to provide a satisfying solution. However, some fusion methods based on the traditional Linear Discriminant Analysis (LDA) may cause the singular matrix problem. Therefore, a Fishervoice based feature fusion method incorporating with the Principal Component Analysis (PCA) and the LDA is proposed, where several features, such as MFCC, PLAR and LPCC, which are commonly used, are concatenated, and then projected into a lower-dimensional subspace. Compared with the baseline GMM-UBM systems using any single feature and using the LDA based fusion method, the proposed one can effectively reduce the equal error rate and give the best performance for text-independent speaker recognition for utterances as short as about 2 seconds.
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