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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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Toward an accurate mobility trajectory recovery using contrastive learning

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Frontiers of information Technology & Electronic Engineering 2024年第11期25卷 1479-1496页

作者： Yushan LIU Yang CHEN Jiayun ZHANG Yu XIAO Xin WANG Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Department of Information and Communications Engineering Aalto UniversityEspoo FI-02150Finland

Human mobility trajectories are fundamental resources for analyzing mobile behaviors in urban computing ***,these trajectories,typically collected from location-based services,often suffer from sparsity and irregularity in *** support the development of mobile applications,there is a need to recover or estimate missing locations of unobserved time slots in these trajectories at a fine-grained spatial–temporal *** methods for trajectory recovery rely on either individual user trajectories or collective mobility patterns from all *** potential to combine individual and collective patterns for precise trajectory recovery remains ***,current methods are sensitive to the heterogeneous temporal distributions of the observable trajectory *** this paper,we propose CLMove(where CL stands for contrastive learning),a novel model designed to capture multilevel mobility patterns and enhance robustness in trajectory *** features a two-stage location encoder that captures collective and individual mobility *** graph neural network based networks in CLMove explore location transition patterns within a single trajectory and across various user *** also design a trajectory-level contrastive learning task to improve the robustness of the *** experimental results on three representative real-world datasets demonstrate that our CLMove model consistently outperforms state-of-the-art methods in terms of trajectory recovery accuracy.

关键词： Human mobility Mobility trajectory recovery Contrastive learning

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HARNESSING JOINT RAIN-/DETAIL-AWARE REPRESENTATIONS TO ELIMINATE INTRICATE RAINS 12

HARNESSING JOINT RAIN-/DETAIL-AWARE REPRESENTATIONS TO ELIMI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ran, Wu Ma, Peirong He, Zhiquan Ren, Hao Lu, Hong School of Computer Science Fudan University China Shanghai Key Lab of Intelligent Information Processing China

Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to suboptimal results. To overcome this limitation, we focus on addressing various rainy images by delving into meaningful representations that encapsulate both the rain and background components. Leveraging these representations as instructive guidance, we put forth a Context-based Instance-level Modulation (CoI-M) mechanism adept at efficiently modulating CNN- or Transformer-based models. Furthermore, we devise a rain-/detail-aware contrastive learning strategy to help extract joint rain-/detail-aware representations. By integrating CoI-M with the rain-/detail-aware Contrastive learning, we develop CoIC, an innovative and potent algorithm tailored for training models on mixed datasets. Moreover, CoIC offers insight into modeling relationships of datasets, quantitatively assessing the impact of rain and details on restoration, and unveiling distinct behaviors of models given diverse inputs. Extensive experiments validate the efficacy of CoIC in boosting the deraining ability of CNN and Transformer models. CoIC also enhances the deraining prowess remarkably when real-world dataset is included. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Rain

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Scene-Adaptive Reference Selection and Inter Prediction for Gaming Video Coding 5

Scene-Adaptive Reference Selection and Inter Prediction for ...

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5th International Seminar on Artificial Intelligence, Networking and information Technology, AINIT 2024

作者： Lin, Chengda Lin, Simin Fang, Ying Yi, Shiqi Wei, Hongan Xu, Yiwen Fuzhou University Fujian Key Lab For Intelligent Processing and Wireless Transmission of Media Information Fuzhou350108 China

ISBN: (纸本)9798350385557

With the burgeoning growth of the e-sports industry, there has been a rapid proliferation of gaming videos on online platforms. Simultaneously, this surge undoubtedly presents significant challenges to the encoding of game videos. However, the recurring characteristic of gaming videos is not efficiently studied in the current standardization, such as Versatile Video Coding (VVC). Based on these observations, our general framework, utilizing the Structural SIMilarity index metric (SSIM), Hash SIMilarity index metric (HSIM), and Hash Matrix SIMilarity index metric (HMSIM), identifies recurring video clips and adjusts the reference frame of the first frame. Additionally, we analyze recurring patterns in different resolutions and types, proposing our Scene Adaptation (SA) optimization algorithm, which integrates SSIM, HSIM, and HMSIM to adapt to various resolutions and scenes. Experimental results show that the proposed approach can achieve a -4.71 % Bjontegaard Delta Bit Rate (BDBR) and 1.00% Time Save (TS), outperforming the benchmark. © 2024 IEEE.

关键词： Video signal processing

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Exploring the Feasibility and Challenges of Treating Follow-up Patients via a Mobile Platform in China

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Proceedings of the ACM on Human-Computer Interaction 2025年第2期9卷 1-31页

作者： Fu, Jiaojiao Zhou, Yangfan Wang, Xin Guo, Yi School of Information Science and Engineering East China University of Science and Technology School of Computer Science Fudan University China School of Computer Science Fudan University China Shanghai Key Lab. of Intelligent Information Processing China

Mobile technology is being increasingly adopted in teleconsultation for its convenience and mobility. Although widely accepted by physicians for informal online consultations, the effectiveness of mobile platforms in formal medical interventions, such as follow-up treatments, remains largely underexplored. This research presents a case study from a Chinese hospital, examining how physicians use mobile platforms to treat chronic patients, the challenges they encounter, and their overall experience. The study aims to determine whether mobile technology can enable physicians to fulfill their responsibilities in managing chronic disease follow-ups online. Through observations and interviews, we found that using mobile platforms introduces significant challenges. Physicians operate in complex and varying environments, such as workplaces, homes, and even public areas, making them struggle to access comprehensive information, communicate effectively, and maintain detailed medical records. As a result, physicians face poor working conditions, reduced efficiency, and struggle to make accurate treatment decisions. This study highlights the need for policy reforms and technological innovations to ensure sustainable teleconsultation practices on mobile platforms. It contributes to the HCI and CSCW communities by highlighting the feasibility of using mobile platforms in online chronic disease follow-ups. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： mHealth

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An Enhanced Particle Swarm Algorithm Based on Fitness Landscape information 13

An Enhanced Particle Swarm Algorithm Based on Fitness Landsc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Diao, Zhenya Yu, Fei Guan, Jian Liu, Meitong School of Physics and Information Engineering Minnan Normal University Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China School of Physics and Information Engineering Minnan Normal University Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China

ISBN: (纸本)9798350308365

In the original Particle Swarm Optimization (PSO), the selection of parameters significantly impacts the search capability of PSO. The paper introduces an innovative algorithm called Fitness Landscape-based PSO (FLPSO) to address the impact of parameter selection on the search capability of PSO. The algorithm focuses on acquiring fitness landscape information and analyzing the population state to intelligently select appropriate parameters in each generation. FLPSO defines four primary states: exploration, exploitation, convergence, and escaping state. In the convergence state, an elite learning strategy is incorporated to help the algorithm escape potential local optima by targeting the global best particle. To validate the efficacy of FLPSO, the paper conducted experiments on 30 different characteristic test functions from CEC2017. FLPSO was compared with six existing algorithms: PSO, PSO-TVAC, APSO, AWPSO, RPSO and ASRPPSO. The experimental results showed significant improvements in FLPSO, providing promising evidence for its effectiveness. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Geometry-Aware 3D Salient Object Detection Network 39

Geometry-Aware 3D Salient Object Detection Network

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Chen Zhang, Liyuan Hui, Le Liu, Qi Dai, Yuchao Shaanxi Key Laboratory of Information Acquisition and Processing Northwestern Polytechnical University China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

Point cloud salient object detection has attracted the attention of researchers in recent years. Since existing works do not fully utilize the geometry context of 3D objects, blurry boundaries are generated when segmenting objects with complex backgrounds. In this paper, we propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints to enhance the geometric boundaries of objects, thereby segmenting complete objects with clear boundaries. Specifically, we first propose a simple yet effective superpoint partition module to cluster points into superpoints. In order to improve the quality of superpoints, we present a point cloud class-agnostic loss to learn discriminative point features for clustering superpoints from the object. After obtaining superpoints, we then propose a geometry enhancement module that utilizes superpoint-point attention to aggregate geometric information into point features for predicting the salient map of the object with clear boundaries. Extensive experiments show that our method achieves new state-of-the-art performance on the PCSOD dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geometry

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Fairness-aware Prompt Tuning for Graph Neural Networks 25

Fairness-aware Prompt Tuning for Graph Neural Networks

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34th ACM Web Conference, WWW 2025

作者： Li, Zhengpin Lin, Minhua Wang, Jian Wang, Suhang Fudan University Shanghai China The Pennsylvania State University State College United States Shanghai Key Lab of Intelligent Information Processing China

ISBN: (纸本)9798400712746

Graph prompt tuning has achieved significant success for its ability to effectively adapt pre-trained graph neural networks to various downstream tasks. However, the pre-trained models may learn discriminatory representation due to the inherent prejudice in graph-structured data. Existing graph prompt tuning overlooks such unfairness, leading to biased outputs towards certain demographic groups determined by sensitive attributes such as gender, age, and political ideology. To overcome this limitation, we propose a fairness-aware graph prompt tuning method to promote fairness while enhancing the generality of any pre-trained GNNs (named FPrompt). FPrompt introduces hybrid graph prompts to augment counterfactual data while aligning the pre-training and downstream tasks. It also applies edge modification to increase sensitivity heterophily. We provide a two-fold theoretical analysis: first, we demonstrate that FPrompt possesses universal capabilities in handling pre-trained GNN models across various pre-training strategies, ensuring its adaptability in different scenarios. Second, we show that FPrompt effectively reduces the upper bound of generalized statistical parity, thereby mitigating the bias of pre-trained models. Extensive experiments demonstrate that FPrompt outperforms baseline models in both accuracy and fairness (~33%) on benchmark datasets. Additionally, we introduce a new benchmark for transferable evaluation, showing that FPrompt achieves state-of-the-art generalization performance. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Impact of COVID-19 on Residential and Non-residential Power Consumption: A Data-Driven Study 8

Impact of COVID-19 on Residential and Non-residential Power ...

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8th Asia Conference on Power and Electrical Engineering, ACPEE 2023

作者： Zhang, Qifan Cao, Xiu Chen, Yang Cao, Yixin Gao, Min Fudan University Shanghai Key Lab of Intelligent Information Processing Department of Computer Science Shanghai China

ISBN: (纸本)9798350345520

In the past few years, the COVID-19 pandemic has affected all aspects of people's daily life. In particular, people in large cities have also been greatly affected. How to accurately and timely evaluate and analyze the impact of COVID-19 on large cities in China is increasingly significant. Based on the four-year power consumption data of a large city in China, this paper examines COVID-19's impact from the perspective of residential and non-residential power *** address the discrepancies in climate and date information, we created COVID-19-Power, a framework that predicts the predicted power consumption of the city in 2020 without the outbreak of COVID-19, and then analyzes the difference in power consumption between the real situation and predicted results. For residential power consumption, we analyze the respective users' behavior citywide, in different districts, and with different historical power consumption habits clustered by the K-means algorithm. On the other hand, for non-residential consumption, we analyze the users citywide, in different regions and industries. The results showed that while the impact of the pandemic on resident power consumption has been small, it is significant for non-residential power consumption, which is independent of geographical location. The regional differences were mainly reflected in the main types of industries. Through these research results, we can understand in detail the impact of the pandemic on residents' lives and industries in China's large cities;Power enterprises can more precisely understand the impact of the pandemic on electrical load, so that power enterprises can adjust power supply strategies more effectively and pertinently. © 2023 IEEE.

关键词： COVID-19

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Non-uniform Compressive Sensing Imaging Based on Image Saliency

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Chinese Journal of Electronics 2023年第1期32卷 159-165页

作者： LI Hongliang DAI Feng ZHAO Qiang MA Yike CAO Juan ZHANG Yongdong University of Chinese Academy of Sciences Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences University of Science and Technology of China

For more effective image sampling, compressive sensing(CS) imaging methods based on image saliency have been proposed in recent years. Those methods assign higher measurement rates to salient regions,but lower measurement rate to non-salient regions to improve the performance of CS imaging. However, those methods are block-based, which are difficult to apply to actual CS sampling, as each photodiode should strictly correspond to a block of the scene. In our work, we propose a non-uniform CS imaging method based on image saliency, which assigns higher measurement density to salient regions and lower density to non-salient regions,where measurement density is the number of pixels measured in a unit size. As the dimension of the signal is reduced, the quality of reconstructed image will be improved theoretically, which is confirmed by our experiments. Since the scene is sampled as a whole, our method can be easily applied to actual CS sampling. To verify the feasibility of our approach, we design and implement a hardware sampling system, which can apply our non-uniform sampling method to obtain measurements and reconstruct the images. To our best knowledge, this is the first CS hardware sampling system based on image saliency.

关键词： Measurement units Density measurement Imaging Image sampling Size measurement Hardware Sensors

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