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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenjun Li Wanjun Ma Mengyun Yang Xiwei Tang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Computer Science Hunan First Normal University Changsha China School of Intelligent Manufacturing Hunan First Normal University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug repositioning is a vital area of biomedicine, where confirming interactions between drugs and specific targets is essential for establishing the efficacy of pharmaceutical agents. Traditional in vitro screening methods have limitations, prompting the use of computer simulations as an effective alternative for predicting drug-target interactions (DTI). This approach has gained significant attention in the scientific community. In this study, we introduce MFCM-DTI, a DTI prediction model that employs multimodal features to accurately capture the intricate interactions between drug molecular structures and key amino acids of target proteins. Our results demonstrate that MFCM-DTI outperforms existing models in prediction accuracy and robustness. Furthermore, MFCM-DTI has been successfully used to predict interactions between key SARS-CoV-2 proteins and existing drugs, providing a solid foundation for developing therapeutic agents against SARS-CoV-2 infection. This study underscores the broad applicability and strong predictive capabilities of MFCM-DTI in drug-protein interaction prediction, opening new avenues for research. The predicted drug targets and interaction data offer valuable insights for future experimental validation and clinical trials, potentially driving innovation in the biomedical field.

关键词： Drugs Proteins Technological innovation Accuracy Predictive models Feature extraction Solids Robustness Coronaviruses Diffusion tensor imaging

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Multi-task regression learning for survival analysis via prior information guided transductive matrix completion

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Frontiers of Computer Science 2020年第5期14卷 99-112页

作者： Lei Chen Kai Shao Xianzhong Long Lingsheng Wang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210023China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and AstronauticsNanjing211106China

Survival analysis aims to predict the occurrence time of a particular event of interest,which is crucial for the prognosis analysis of ***,due to the limited study period and potential losing tracks,the observed data inevitably involve some censored instances,and thus brings a unique challenge that distinguishes from the general regression *** addition,survival analysis also suffers from other inherent challenges such as the high-dimension and small-sample-size *** address these challenges,we propose a novel multi-task regression learning model,i.e.,prior information guided transductive matrix completion(PigTMC)model,to predict the survival status of the new ***,we use the multi-label transductive matrix completion framework to leverage the censored instances together with the uncensored instances as the training samples,and simultaneously employ the multi-task transductive feature selection scheme to alleviate the overfitting issue caused by high-dimension and small-sample-size *** addition,we employ the prior temporal stability of the survival statuses at adjacent time intervals to guide survival ***,we design an optimization algorithm with guaranteed convergence to solve the proposed PigTMC ***,the extensive experiments performed on the real microarray gene expression datasets demonstrate that our proposed model outperforms the previously widely used competing methods.

关键词： survival analysis matrix completion multi-task regression transductive learning multi-task feature selection

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Hybrid Convolutional-Transformer Neural Network for Driver Distraction Detection

Hybrid Convolutional-Transformer Neural Network for Driver D...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Penghua Li Qiyun Mou Jie Hou Yushan Tu Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

Distracted driving causes a significant number of traffic accidents each year, making it a crucial area of research to monitor the driver’s state and prevent accidents. Although convolutional networks are widely used for detecting driver distraction, they face several issues, such as poor generalization ability, high model capacity, and response latency. To tackle these challenges, this paper introduces a nimble and low-latency algorithm named MViTCNet, which utilizes the MobileViT module for the identification of driver distraction while operating within constrained computational resources. The MViTCNet algorithm extracts local features and constructs an original feature map, which is fused with a global feature representation processed by a Transformer to capture finer details and obtain a larger receptive field. Additionally, the classic convolutional module MBConv is employed to reduce spatial redundancy, and downsampling is used to obtain low-resolution feature maps. By stacking multiple modules to learn more complex feature representations, the network structure increases in depth and width, resulting in a better fit for complex data distributions. Experiments on an embedded hardware platform show that MViTCNet achieves an acceptable trade-off, with an accuracy of 91.04% on the StateFarm dataset and 97.80% on the LDDB dataset at a latency of 28.9±5.3 milliseconds, outperforming existing methods.

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APPLICATION TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS ARRAY GEOMETRIES: THE NON-VANDERMONDE ATOMIC SOFT THRESHOLDING APPROACH

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS A...

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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & Technology 2023.

关键词： Automotive radar

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Enhancing EEG-Based Cross-Subject Emotion Recognition via Adaptive Source Joint Domain Adaptation

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IEEE Transactions on Affective computing 2024年

作者： Liu, Ke Luo, Xin Zhu, Wenrui Yu, Zhuliang Yu, Hong Xiao, Bin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Shanghai Jiao Tong University School of Medicine Department of Neurology Songjjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

EEG emotion recognition is crucial in both human-machine interaction and healthcare. However, recognizing emotions across different subjects remains challenging due to individual variability. While existing multi-source domain adaptation methods have been utilized for cross-subject EEG emotion decoding, they often struggle with irrelevant or weakly relevant source domains, leading to negative transfer. Additionally, variations within subdomains are often neglected in these studies. We propose a joint domain adaptation method, Adaptive Source Joint Domain Adaptation (ASJDA) to address these issues. ASJDA utilizes an unsupervised adaptive source selection strategy to select a subset of source domains by evaluating the Jensen-Shannon divergence between the source and target domains, choosing those most relevant to the target. Subsequently, it implements joint domain adaptation with these chosen sources at both the domain and category subdomain levels. Our proposed method outperforms existing state-of-the-art methods, achieving cross-subject accuracies of 96.81% in SEED, 89.69% in SEED-IV, and 69.31% in DEAP. This work significantly advances the state of the art in EEG emotion recognition by effectively addressing the challenges of cross-subject variability. The source code for ASJDA is available at https://***/Pam098/ASJDA. © 2010-2012 IEEE.

关键词： adaptive source selection cross-subject domain adaptation EEG emotion recognition

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Learning Counterfactual Explanation of Graph Neural Networks Via Generative Flow Network

SSRN

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SSRN 2023年

作者： He, Kangjia Liu, Li Zhang, Youmin Wang, Ye Liu, Qun Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

Counterfactual subgraphs explain Graph Neural Networks (GNNs) by answering the question: "How would the prediction change if a certain subgraph were absent in the input instance?" The differentiable proxy adjacency matrix is prevalent in current counterfactual subgraph discovery studies due to its ability to avoid exhaustive edge searching. However, a prediction gap exists when feeding the proxy matrix with continuous values and the thresholded discrete adjacency matrix to GNNs, compromising the optimization of the subgraph generator. Furthermore, the end-to-end learning schema adopted in the subgraph generator limits the diversity of counterfactual subgraphs. To this end, we propose CF-GFNExplainer, a flow-based approach for learning counterfactual subgraphs. CF-GFNExplainer employs a policy network with a discrete edge removal schema to construct counterfactual subgraph generation trajectories. Additionally, we introduce a reward and loss function designed to guide CF-GFNExplainer's optimization. The discrete adjacency matrix generated in each trajectory eliminates the prediction gap, enhancing the validity of the learned subgraphs. Furthermore, the multi-trajectories sampling strategy adopted in CF-GFNExplainer results in diverse counterfactual subgraphs. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in terms of validity and diversity. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Semi-Supervised Feature Selection Based on Fuzzy Related Family

SSRN

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SSRN 2023年

作者： Yang, Tian Guo, Zhijun Li, Yuan-Jiang Deng, Yanfang Qian, Yuhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China

Existing machine learning algorithms face the problems of label-missing and high dimensionality. Feature selection is an effective dimensionality reduction method that can improve the efficiency and accuracy of subsequent machine learning tasks by removing irrelevant and redundant features. Related family is an efficient and rough set-based feature selection method, but is difficult to be used in simisupervised learning tasks. However, due to the difficulty of obtaining all labeled data, partially labeled data has become an important object that machine learning models must deal with. Therefore, this paper proposes a semi-supervised fuzzy related family-based feature selection method for partially labeled data. This method first calculates the fuzzy label values of unlabeled samples through fuzzy similarity relationships to establish a new fuzzy covering system. Then, based on the fuzzy covering system, it constructs a fuzzy related family based on consistent fuzzy set. Finally, based on the constructed feature significance measurement, a semi-supervised feature selection algorithm based on fuzzy related family (SFRF) is established. Compared with existing semi-supervised feature selection algorithms, SFRF can significantly improve the efficiency of feature selection while maintaining classification accuracy, with the average reduction efficiency of nine datasets increased by up to 201 times. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Breath of Life: A Biofeedback Game with Diverse Breathing Techniques for Enhanced Real-Life Emotion Regulation

Breath of Life: A Biofeedback Game with Diverse Breathing Te...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Chen, Qi Du, Jiachen Yao, Zhihao Feng, Yuanling Liu, Guanhong Ni, Ning Wu, Zhenyu Liu, Haoran Chen, Xijing Mi, Haipeng Wuhan Textile University Wuhan China Tsinghua University Beijing China The Future Laboratory Tsinghua University Beijing China College of Design and Innovation Intelligent Big Data Visualization Lab. Tongji University Shanghai China Department of Psychology University of Chinese Academy of Sciences Beijing China Key Laboratory of Cognitive Science and Mental Health Institute of Psychology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400713958

Mental health is a critical public health concern, with emotion regulation (ER) playing a pivotal role in maintaining and improving psychological well-being. While respiratory biofeedback games have emerged as effective tools for ER training, existing research predominantly focuses on single-session interventions with limited techniques, emphasizing short-term skill transfer in simulated environments. This approach has led to a significant gap in understanding the long-term application of these techniques in real-life contexts, which is essential for designing effective ER training games. To address this gap, we developed a biofeedback game incorporating four distinct breathing techniques and conducted a 10-week longitudinal study with 25 participants. After one week of training, the majority of participants demonstrated successful transfer of breathing techniques to real-life scenarios. Our findings provide actionable design insights for promoting long-term skill transfer and highlight the potential of respiratory biofeedback games as sustainable tools for emotion regulation training. © 2025 Copyright held by the owner/author(s).

关键词： Biofeedback

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