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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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Indoor Accidents Detection for Elderly Based on Features Fusion and LSTM 21

Indoor Accidents Detection for Elderly Based on Features Fus...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： San, Win Pa Pa Zaw, Sai Maung Maung University of Computer Studies Mandalay Image and Signal Processing Lab Mandalay Myanmar University of Computer Studies Mandalay Faculty of Computer Systems and Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

Nowadays, with aging of the human society, the 'ratio of family care givers and elderly' is not equivalent and cannot give enough caring to the elderly in some countries. Therefore, automatic health monitoring system for elderly draws the interests of many researchers and become as special issue. In the implementation of elderly people monitoring system, the detection of fall events plays as an important role because the fall is the common cause which is very risky and threaten the health of elderly. The aim of this research is to implement the vision-based fall detection system which is important for health monitoring of elderly by applying the computer vision and deep learning technology. Most of the traditional vision-based fall detection systems which based on the height, width of human posture, fail to detect the fall events from different camera viewpoints. In this research, to accurately detect the fall from different camera viewpoint, the features fusion approach is applied over the deep learning model. In the implementation of the system, firstly, the human silhouette image is created by using the background subtraction. After that, the human silhouette image which have been extracted from two consecutive frames are fused as a single image called Silhouette History Image (SHI). Then, the SHI results are used as shape feature. On the other hand, the motion features called Dense Optical Flow (DOF) is extracted over the two consecutive video frames and fuse SHI and DOF as a single input image. Then, the results of features fusion are feed as input for pretrained Convolution Neural Network (CNN) for extracting the deep CNN features. Finally, the Recurrent Neural Network (RNN) called Long-Short-Term-Memory (LSTM) is trained over those CNN features for recognizing the sequence of fall event. The experiments are performed using the publicly availab.e dataset of UP-fall detection dataset for evaluating and confirming the effectiveness of the proposed method. © 2024

关键词： Fall detection

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Supervised Relation Extraction Based on lab.ls Contrastive Learning 16

Supervised Relation Extraction Based on Labels Contrastive L...

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16th International Congress on Image and signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Wang, Yuanru Zhao, Yanhui Cui, Rong-Yi Jin, Guo-Zhe Yanbian University Intelligent Information Processing Lab. Department of Computer Science & Technology Yanji China

ISBN: (纸本)9798350330755

Relation extraction is an important task in natural language processing. Existing relation extraction tasks usually use data augmentation to construct positive and negative samples for contrastive learning training. And previous works lack the accurate selection of positive and negative samples and the full use of relation lab.ls. To overcome these defects, for supervised relational extraction, we propose a lab.ls Contrastive Learning framework (lab.lsCL) that considers both global and local perspectives, which makes full use of lab.ls for design. Specifically, from a global perspective, we use lab.l and cosine similarity to select the most easily misclassified samples as positive and negative examples for training. From a local perspective, positive and negative samples are selected according to batch and lab.l, and this way of sample selection increases the randomness of sample selection. The contrastive learning framework lab.lsCL proposed by us makes full use of lab.ls and synthesizes two perspectives to construct contrastive learning lab.lsCL, which increases the tolerance of the framework. Finally, the experimental results demonstrate the effectiveness of our proposed framework, which is significantly improved on the Semeval-2010 Task 8 dataset compared with the baselines. © 2023 IEEE.

关键词： Extraction

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection 18

Effect of Pectoral Muscles on CNN based Mammographic Cancer ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Dhimann, Santoresh Kumari Sawhney, Tinny Yadav, Rakesh Kumar Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India University of Jammu Digital Signal Processing Lab Department of Electronics Jammu India

ISBN: (纸本)9789380544519

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The most common is breast cancer and it is affective affecting one out of every ten women. The reason of breast lesions formation may be calcification or mass deposit and digital mammography is generally used for verifying it. It is a cheap, non-toxic, and user-friendly test, but needs expert opinion for ascertaining the presence of cancer. The accuracy of mammographic detection varies between 80% and 90%. Recently, Convolutional neural networks (CNNs) have shown their application in automatic detection of the cancer, but inclusion of pectoral muscle in the mammogram reduces the efficiency of the CNN system as the texture of the muscle resembles with cancerous tissues. Further, CNN based detection requires high resolution images which in turn requires state of art technology-based machines, generally, not availab.e in rural area-based clinics. The focus of the proposed research is to investigate effect of low-quality blurred input mammograms on the detection of pectoral muscle boundary, so that the pectoral muscle removed mammograms can be applied to CNN system for automatic prediction of cancerous images. The investigations carried out have shown that low quality blurred images can also be employed for detecting cancerous tissues provided the pectoral muscle is removed from the mammograms before using CNN based classifier. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cancer detection CNN Hough lines mammogram pectoral muscle

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Echo Simulation and Application of Multi-Channel Interferometric SAR Based on OptiX

Echo Simulation and Application of Multi-Channel Interferome...

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2024 Conference on Spectral technology and Applications, CSTA 2024

作者： Wu, Jiajun Liu, Peng Zhang, Qi Bai, Zhenhao Li, Zhenfang Zhou, Yashi Ma, Jian National Lab. of Radar Signal Processing Xidian University Xi'an710071 China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing100098 China

ISBN: (纸本)9781510683082

Azimuth multi-channel interferometric synthetic aperture radar (SAR) achieves imaging and DEM product generation while simultaneously ensuring high resolution and wide observation swath. To achieve rapid and precise echo simulation in multi-channel interferometric SAR, a novel approach leveraging OptiX-based simulation is proposed. This method utilizes OptiX, a ray tracing engine, for acceleration, and introduces a multiple scattering precise delay calculation algorithm to ensure high phase accuracy. The simulated results are validated using an echo simulator to verify their authenticity and utility. By fully leveraging the GPU's hardware ray tracing core, this method significantly enhances the computational speed of echo simulations. Moreover, its integration into echo simulators provides invaluable data sources for system debugging of multi-channel interferometric SAR, offering substantial auxiliary benefits for SAR applications, particularly in remote sensing and geospatial analysis. © 2024 SPIE.

关键词： Synthetic aperture radar

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J oint Optimization of Digital Beamforming and RF Interference Cancellation for SBFD

J oint Optimization of Digital Beamforming and RF Interferen...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Hu, Nan Li, Yan Long, Hang Liu, Congxi Ke, Ting Wang, Fei Institute of Wireless and Terminal Technology China Mobile Research Institute Beijing China Wireless Signal Processing and Network Lab. Beijing China

ISBN: (纸本)9798350370218

Subband non-overlapping full duplex (SBFD) re-cently has been proposed to enhance uplink coverage and reduce latency of conventional time division duplex (TDD) systems by allocating frequency non-overlapping uplink (UL) and downlink (DL) subbands within a TDD band. However, if sufficient interference cancellation (IC) is not provided in the spatial and radio frequency (RF) domain, the self-interference (SI) signal from the DL subband will block the UL receiver. In this paper, we propose a two-stage joint optimization of beamforming and RF IC in SBFD systems, specifically tailored for base station deployment. In the first stage, the RF SI canceller is designed based on the SI channel. Two feasible realizations of the RF SI canceller are provided to reduce the number of taps connecting the Tx and Rx RF chains. In the second stage, the baseband precoding is optimized based on the equivalent residual SI channel and the user channel. Simulation results show that the proposed algorithm can support up to 4 layers of precoding with a maximum rate loss of 20.11% in the DL and up to 5 layers of precoding with a rate loss of less than 10% in the UL. © 2023 IEEE.

关键词： beamforming RF interference cancellation subband non-overlapping full duplex

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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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Adversarial Example Generation Method Based on Probability Histogram Equalization 42

Adversarial Example Generation Method Based on Probability H...

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42nd Chinese Control Conference, CCC 2023

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

ISBN: (纸本)9789887581543

CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Iterative methods

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Machine Learning Approaches for Automated Detection and Classification of Dysarthria Severity 2

Machine Learning Approaches for Automated Detection and Clas...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Hamza, Amina Addou, Djamel Kheddar, Hamza University of Sciences and Technology Houari Boumediene Speech and Signal Processing Lab Algiers Algeria University of Medea Lsea Lab. Faculty of Technology Dept. Electrical Engineering Medea Algeria

ISBN: (纸本)9798350314243

Dysarthria, a speech disorder caused by neuro-motor problems resulting in impaired articulation, requires an assessment of its severity for diagnostic and monitoring purposes. Additionally, accurate severity classification facilitates the development of automated dysarthric speech detection and classification systems. This paper presents a comprehensive investigation into detecting dysarthric voices within a collection of normal voice samples, followed by the dysarthria severity classification utilizing neural network frameworks, specifically long short-term memory network (LSTM) and recurrent neural network (RNN). The study employs various features including Mel frequency cepstral coefficients (MFCC), formants, prosodic parameters, and voice quality. The performance of these models is evaluated against a baseline support vector machine (SVM) classifier using the Nemours corpus database. Remarkably, the highest classification accuracy achieved for this corpus is 99.69%. Detailed analysis demonstrates that selecting an appropriate neural network architecture yields superior performance compared to the conventional SVM classifier. © 2023 IEEE.

关键词： Support vector machines

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ϵ-π: A Nonparametric Model for Neural Power Spectra Decomposition

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IEEE Journal of Biomedical and Health Informatics 2024年第5期28卷 2624-2635页

作者： Hu, Shiang Zhang, Zhihao Zhang, Xiaochu Wu, Xiaopei Valdes-Sosa, Pedro A. Anhui University Anhui Provincial Key Lab of Multimodal Cognitive Computation Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China University of Science and Technology of China School of Humanities and Social Science Department of Psychology Hefei230026 China University of Electronic Science and Technology of China MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu611731 China

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks;the sleep EEG revealed that produced more distinguishable AC exponents and improved the sleep state classification accuracy;the iEEG showed that approached the clinical findings in peak discovery. Overall, offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases. © 2013 IEEE.

关键词： Electroencephalography

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