MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of compu...
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MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.
作者：
Liu, YongkangPan, DonghuiZhang, HaifengZhong, KaiAnhui University
Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education School of Mathematical Sciences Hefei230601 China Anhui University
Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China
Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated...
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This article presents a Symplectic Geometric Mode Decomposition (SGMD) method incorporating K-means clustering, coupled with wavelet denoising, for mitigating noise in Linear Frequency Modulation (LFM) signals. This m...
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作者：
Huang, QingFan, YuanCheng, SongsongAnhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui Hefei230601 China
In this paper, we study a category of distributed constrained optimization problems where each agent has access to local information, communicates with its neighbors, and cooperatively minimizes the aggregated cost fu...
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The paper proposes a noise reduction algorithm based on symplectic Geometric Mode Decomposition (SGMD) and Savitzky-Golay (SG)filtering to address the issue of noise interference during signal transmission. Firstly, t...
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We propose a robust pipeline detection algorithm for obscuration environments, which includes two improvements: a pre-processing method called the Regional Adaptive Thresh-olding Algorithm (RATA) and a novel clusterin...
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The paper introduces a Kernel Weibull M-Transform Least-Mean Square (LMS) (KWMLMS) algorithm aimed to enhance filtering performance for a nonlinear system. By incorporating the Weibull M-Transformation into the cost f...
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The phase recovery method based on the Transport of Intensity Equation is widely used in the field of microscopic imaging of biological cells. When using this method for phase recovery, the CCD is always moved to acqu...
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Fourier ptychographic microscopy (FPM) combines the concepts of phase retrieval algorithms and synthetic apertures and can solve the problem in which it is difficult to combine a large field of view with high resoluti...
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Fourier ptychographic microscopy (FPM) combines the concepts of phase retrieval algorithms and synthetic apertures and can solve the problem in which it is difficult to combine a large field of view with high resolution. However, the use of the coherent transfer function in conventional calculations to describe the linear transfer proc-ess of an imaging system can lead to ringing artifacts. In addition, the Gerchberg-Saxton iterative algorithm can cause the phase retrieval part of the FPM algorithm to fall into a local optimum. In this paper, Gaussian apodization coherent transfer function is proposed to describe the imaging process and is combined with an iterative method based on amplitude weighting and phase gradient descent to reduce the presence of ringing artifacts while ensuring the accuracy of the reconstructed results. In simulated experiments, the proposed algorithm is shown to give a smaller mean square error and higher structural similarity, both in the presence and absence of noise. Finally, the proposed algorithm is validated in terms of giving reconstruction results with high accuracy and high resolution, using images acquired with a new microscope system and open-source images. (c) 2023 Optica Publishing Group
In this paper, a novel and comprehensive signal denoising method is proposed by combining Symplectic Geometric Modal Decomposition (SGMD) and Block Thresholding denoising. The proposed approach involves a three-step p...
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