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DMFVAE:miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

Frontiers of Computer Science 2024年第6期18卷 259-270页

作者： Pijing WEI Qianqian WANG Zhen GAO Ruifen CAO Chunhou ZHENG Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China

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.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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Enhanced Symplectic Geometric Mode Decomposition via K-means Clustering for LFM signal Denoising 7

Enhanced Symplectic Geometric Mode Decomposition via K-means...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Ni, Jincheng Zhao, Jin Wu, Xianliang Wu, Ping AnHui University Key Lab of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9798350384437

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 method substitutes the original component recombination approach in SGMD with the K-means clustering algorithm, thus rendering the signal grouping more logically coherent. In the experiment, we compare the method proposed in this article with other denoising methods. The experimental findings demonstrate substantial advantages of the proposed method in mitigating noise in LFM signals, thereby enhancing both signal-to-Noise Ratio (SNR) and signal quality. The research outcomes underscore the feasibility and substantial potential of this method in mitigating noise in signals. In subsequent studies, additional investigation into its applicability across diverse domains can be pursued alongside the refinement of clustering and denoising algorithms. © 2024 IEEE.

关键词： Wavelet decomposition

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Research on signal Denoising Method Based on Symplectic Geometric Mode Decomposition and Savitzky-Golay Filtering 7

Research on Signal Denoising Method Based on Symplectic Geom...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Xu, Xuelong Zhao, Jin Wu, Bo Wu, Xianliang Ni, Jincheng Wu, Ping Ministry of Education Anhui University Anhui University Key Lab of Intelligent Computing & Signal Processing Hefei China

ISBN: (纸本)9798350384437

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, the noisy signal is decomposed using symplectic geometric mode decomposition to obtain several symplectic geometric components. The component with less noise is then selected and superimposed based on its energy entropy. Finally, the reconstructed signal undergoes denoising through SG filtering. The proposed method is applied to conduct comparison experiments on noise reduction for linear frequency modulation signals and multicomponent periodic signals. The results demonstrate a significant noise reduction effect of the proposed method, which holds great significance for signal analysis and research on noise reduction. © 2024 IEEE.

关键词： Noise abatement

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Linear Frequency Modulated signal Denoising Based on Symplectic Geometric Mode Decomposition and Block Thresholding 23

Linear Frequency Modulated Signal Denoising Based on Symplec...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Ni, Jincheng Zhao, Jin Wu, Xianliang AnHui University Key Lab of Intelligent Computing & Signal Processing Ministry of Education Heifei China

ISBN: (纸本)9798350325959

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 process: first, the signal is decomposed into a set of Symplectic Geometric Components (SGCs) using SGMD. Subsequently, each SGC is subjected to Block-Thresholding denoising. Finally, the denoised SGCs are recombined to obtain the denoised linear frequency modulation (LFM) signal. The experimental verification demonstrates the effectiveness of the SGMD-BT method in denoising LFM signals. This novel approach offers a fresh solution for the processing and analysis of LFM signals, holding significant application potential and research importance. © 2023 IEEE.

关键词： Block Thresholding denoising signal denoising Symplectic Geometric Modal Decomposition

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FastICA based single-channel radar signal separation algorithm

FastICA based single-channel radar signal separation algorit...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Xinyu Zhao, Jin Wu, Xianliang Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781839539954

Aiming at the traditional blind source separation for the received single-channel mixed radar signal makes it difficult to accurately recover the source signal, a single-channel radar signal comprehensive separation method based on the combination of the FastICA algorithm and the variational modal decomposition (VMD) algorithm is proposed. The method utilizes VMD to first perform modal separation of single-channel mixed Linear Frequency Modulation (LFM) signal, then selects the modal component signal with the largest correlation coefficient with the mixed signal and expands it with single-channel mixed LFM signal into a virtual multi-channel mixed signal, and finally inputs it into FastICA algorithm to obtain the source signal. The simulation experiment results show that the comprehensive separation algorithm can accurately separate the LFM signal, overcoming the limitation that the FastICA algorithm cannot directly separate the single-channel mixed radar signal. © The Institution of Engineering & Technology 2023.

关键词： Blind source separation

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A Miniaturized Bandwidth Reconfigurable Bandpass Chip Filter with Semi-lumped Topology and GaAs pHEMT Technology

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Chinese Journal of Electronics 2021年第1期30卷 192-198页

作者： LI Linqing ZHU Haoran NING Xingyu LI Zunwen WU Xianliang Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University

A miniaturized reconfigurable bandpass chip filter with semi-lumped topology and Gallium Arsenide pseudomorphic High electron mobility transistor（GaAs pHEMT） technology is proposed. Semi-lumped topology is employed to instead the traditional lumped inductor with microstrip transmission line, which can reduced the size of the tunable filter significantly. Three-order series and shunt resonated bandpass filter is implemented with shorted stubs and metal-insulator-metal capacitors. Two transmission zeros are introduced with the series resonator and the shunted GaAs FET. By tuning the gate bias circuit of the FET, the capacitance of the series resonator is changed and the bandwidth of the filter is adjusted correspondingly. An equivalent circuit model is developed to interpret the mechanism of the proposed filter circuit. A reconfigurable on chip filter sample operated at 10 GHz is fabricated to validate the design. Two fractional bandwidth of 14.3% and 23.5% are tuned with bias voltage of the FET, while insertion loss of 2.4 d B and 2.2 d B are observed with the filter, respectively. The area of the chip filter is 0.86 × 0.96 mm2and is equivalent to an electrical length of 0.08 × 0.09λg2at center frequency. Measurement results agree well with the simulation ones.

关键词： gallium arsenide tunable filter transmission zeros equivalent circuit model resonator filters shunted FET III-V semiconductors miniaturized bandwidth reconfigurable bandpass chip filter MIM devices microstrip transmission line chip filter sample shunt resonated bandpass filter metal-insulator-metal capacitors semilumped topology loss 2.4 dB Gallium Arsenide pseudomorphic High electron mobility transistor technology insertion loss filter circuit integrated circuit design GaAs integrated circuit manufacture frequency 10.0 GHz pHEMT technology microwave filters loss 2.2 dB HEMT integrated circuits series resonator miniaturized reconfigurable bandpass chip filter band-pass filters

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A Wideband GCPW-SIW Fed Slots Antenna for Millimeter-Wave Applications

A Wideband GCPW-SIW Fed Slots Antenna for Millimeter-Wave Ap...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Run, Penghao Sun, Yufa Anhui University Key Lab of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

A wideband GCPW-SIW fed slots antenna in millimeter wave band is designed in this paper. The structure is composed of four inclined slots which are placed on the one side of the SIW with three shorting vias and fed by GCPW transmission line. The simulated results show that the operating frequency band of the proposed antenna is 24.15-27.59GHz (13.23%), the antenna has a stable gain with a maximum value of 9.67dBi and high radiation efficiency. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Slot antennas

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Broadband Circularly Polarized Hybrid Metasurface Antenna for Wearable Devices

Broadband Circularly Polarized Hybrid Metasurface Antenna fo...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Liu, Zhiyi Sun, Yufa Anhui University Key Lab of Intelligent Computing & Signal Processing Ministry of Education HeFei230601 China

ISBN: (纸本)9781733509657

A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip patch. A hybrid MTS is placed above a square patch with cut corners, achieving broadband radiation performance. The working mechanism of hybrid MTS is analyzed by characteristic mode analysis (CMA). The simulated -10dB bandwidth is 40.1% (4.61-6.82 GHz) and the 3dB axial ratio bandwidth is 25.2% (5.16-6.55 GHz) with a peak gain of 8.1 dBi. In addition, the antenna has a front-to-back ratio of approximately 20 dB, which makes it suitable for wearable applications. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Bandwidth

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Reversible Data Hiding for 3D Mesh Model Based on Block Modulus Encryption and Multi-MSB Prediction 10th

Reversible Data Hiding for 3D Mesh Model Based on Block Modu...

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10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Fu, Zihao Gao, Yang Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819787456

Reversible data hiding in encrypted domain (RDH-ED) fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility. Current research on RDH-ED focuses on 2D images, while research on 3D mesh models is still immature. This paper introduces an RDH-ED method using block modulus encryption and multi-MSB prediction. Initially, the original mesh model is partitioned into non-overlapping subblocks of equal size, and then the vertices in each subblock are encrypted with the same key for additive modulus encryption, ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted subblocks. Subsequently, the subblocks are disrupted one by one using the 3D Arnold Transform to enhance security. The vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices, where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable vertex. Finally, the secret information is embedded into the allocated room. Since the proposed method almost completely preserves the spatial correlation within each subblock, the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption (VRAE). The experimental findings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex (bpv), surpassing the state-of-the-art method that achieves 25.63 bpv. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mesh generation

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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