Identification of associations between non-coding RNAs and diseases plays an important role in the study of pathogenesis, which has been a hot topic in recent research. However, traditional methods are timeconsuming t...
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Identification of associations between non-coding RNAs and diseases plays an important role in the study of pathogenesis, which has been a hot topic in recent research. However, traditional methods are timeconsuming to detect the associations between non-coding RNAs and diseases. Recently, associations of non-coding RNAs and diseases can be regarded as bipartite network. In this paper, we propose a novel deep multiple kernel learning method, called the multi-layer multi-kerneldeep neural network (MLMKDNN). First, many feature matrices are built by multiple features of non-coding RNAs and diseases. Then, these feature matrices are mapped into kernel space and fused by deep neural network. Finally, combine two fused output of MLMKDNN as the predicted values. Three types of non-coding RNAs (miRNA, circRNA and lncRNA) are used to test the performance of MLMKDNN. Compared with other existing methods, our proposed model has high Area Under Precision Recall (AUPR) value on three types of datasets. Experimental results confirm that our method is an effective predictive tool. It provides a framework that can also be applied to the link prediction of other bipartite networks. (c) 2022 Elsevier B.V. All rights reserved.
Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper,...
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Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper, to reduce the complexity involved in the medical images and to ameliorate the classification of MRIs, a novel 3D magnetic resonance (MR) brain image classifier using kernel principal component analysis (KPCA) and support vector machines (SVMs) is proposed. Experiments are carried out using A deepmultiplekernel SVM (DMK-SVM) and a regular SVM. An algorithm entitled SVM-KPCA is put forward. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Secondly, KPCA is applied to decrease the dimensionality of features. SVM is then applied to the reduced data. A K-fold cross-validation strategy is used to avoid overfitting and to ameliorate the generalization of the SVM-KPCA algorithm. Three databases are used to validate the suggested SVM-KPCA method. Three conclusions are obtained from this work. First, KPCA is highly efficient in increasing the classifier's performance compared with similar algorithms working on the proposed database. Second, the SVM-KPCA algorithm performs well in differentiating between two classes of medical images. Third, the approach is robust and might be utilized for other MRIs. This proposes a significant role for computer aided diagnosis analysis systems used for clinical practice.
kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. multiplekernellearning (MKL) and Multilayer multiplekernellearning (MLMKL), as new families of kernel me...
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ISBN:
(纸本)9781509004782
kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. multiplekernellearning (MKL) and Multilayer multiplekernellearning (MLMKL), as new families of kernel methods, consist of learning the optimal kernel from a set of predefined kernels by using an optimization algorithm. However, learning this optimal combination is considered to be an arduous task. Furthermore, existing algorithms often do not converge to the optimal solution (i.e., weight distribution). They achieve worse results than the simplest method, which is based on the average combination of base kernels, for some real-world applications. In this paper, we present a hybrid model that integrates two methods: Support Vector Machine (SVM) and multiple Classifier (MC) methods. More precisely, we propose a multiple classifier framework of deep SVMs for classification tasks. We adopt the MC approach to train multiple SVMs based on multiplekernel in a multi-layer structure in order to avoid solving the complicated optimization tasks. Since the average combination of kernels gives high performance, we train multiple models with a predefined combination of kernels. Indeed, we apply a specific distribution of weights for each model. To evaluate the performance of the proposed method, we conducted an extensive set of classification experiments on a number of benchmark data sets. Experimental results show the effectiveness and efficiency of the proposed method as compared to various state-of-the-art MKL and MLMKL algorithms.
kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. multiplekernellearning (MKL) and Multilayer multiplekernellearning (MLMKL), as new families of kernel me...
详细信息
ISBN:
(纸本)9781509004799
kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. multiplekernellearning (MKL) and Multilayer multiplekernellearning (MLMKL), as new families of kernel methods, consist of learning the optimal kernel from a set of predefined kernels by using an optimization algorithm. However, learning this optimal combination is considered to be an arduous task. Furthermore, existing algorithms often do not converge to the optimal solution (i.e., weight distribution). They achieve worse results than the simplest method, which is based on the average combination of base kernels, for some real-world applications. In this paper, we present a hybrid model that integrates two methods: Support Vector Machine (SVM) and multiple Classifier (MC) methods. More precisely, we propose a multiple classifier framework of deep SVMs for classification tasks. We adopt the MC approach to train multiple SVMs based on multiplekernel in a multi-layer structure in order to avoid solving the complicated optimization tasks. Since the average combination of kernels gives high performance, we train multiple models with a predefined combination of kernels. Indeed, we apply a specific distribution of weights for each model. To evaluate the performance of the proposed method, we conducted an extensive set of classification experiments on a number of benchmark data sets. Experimental results show the effectiveness and efficiency of the proposed method as compared to various state-of-the-art MKL and MLMKL algorithms.
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