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Closed-Loop Safe Correction for Reinforcement Learning Policy 1

4th International Conference on Ubiquitous Security, UbiSec 2024

作者： Yi, Zhi Lv, Qi Chen, Shuhong Liang, Ying Dai, Yinglong Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Blockchain Innovation Lab Swinburne University of Technology MelbourneVIC3122 Australia Molecular Nutrition Branch National Engineering Research Center of Rice and By-product Deep Processing College of Food Science and Engineering Central South University of Forestry and Technology Hunan Changsha410004 China

ISBN: (数字)9789819648368

ISBN: (纸本)9789819648351

Trial and error learning is an approach with uncertain consequences. How to maintain policy security, stability, and efficiency under controlled circumstances, posing a significant academic challenge. Such as Reinforcement Learning (RL) leverages the neural network parameter space to identify the optimal solution for achieving the optimal mapping of the policy space. However, even a slight fluctuation in the parameter space can lead to policy collapse. To solve it, we propose a method called Closed-loop Safe Correction (CLSC). This method employs twin agents to establish a closed-loop framework for policy and sampling data distribution, gradually improving policy efficiency while ensuring policy safety. Base on theoretical and experimental analysis, CLSC elucidates the performance, influencing factors, and advantages of this approach in policy safety scenarios. These findings provide novel research insights and practical directions for advancing safe RL. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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GraphIsoFun: a graph neural network based approach for splice isoform function prediction

GraphIsoFun: a graph neural network based approach for splic...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Shuo Zhang Changhuo Yang Hong-Dong Li Jianxin Wang School of Computer Science and Engineering Central South University Changsha China Hunan Hunan Provincial Key Lab on Bioinformatics Central South University Changsha China Hunan

ISBN: (纸本)9781665429825

Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and there are multiple public databases of gene function annotations. Due to the high cost and the large number of isoforms, it is currently impossible to conduct extensive experimental analysis on isoform functions. Functions at the level of splicing isoforms remain largely unknown. Computational prediction provides an alternative to functional annotation of splice isoforms. Here we present GraphIsoFun, a novel computational approach to predict isoform function by integrating both gene and isoform-level expression information with graph neural network. It first constructs a heterogeneous co-expression network with both genes and isoforms being nodes. This network integrates the rich association between isoforms and genes, and provides a bridge for connecting isoform and gene functions. Then GraphIsoFun uses the graph neural network to mine the information of the co-expression network and obtains the final prediction score of isoforms for each function. The experiments on multiple datasets show that GraphIsoFun surpasses existing methods based on different evaluation indicators.

关键词： Proteins Protein engineering Costs Annotations Databases Splicing Conferences

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Improving human essential protein prediction using only protein sequences via ensemble learning

Improving human essential protein prediction using only prot...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Min Zeng Nian Wang Yifan Wu Yiming Li Fang-Xiang Wu Min Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9781665429825

Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://***/CSUBioGroup/EP-GBDT.

关键词： Proteins Protein engineering Costs Codes Conferences Predictive models Biomedical measurement

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Improving the Prediction of Disease-associated Genes by Integrating Annotated Gene Sets

Improving the Prediction of Disease-associated Genes by Inte...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Chao Deng Cui-Xiang Lin Hong-Dong Li Human Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Identifying disease-associated genes is key to studying the pathogenic mechanism of complex diseases. Existing methods for predicting disease genes are dominantly based on molecular networks or omics data, including gene expression, protein expression, co-expression networks, protein-protein interaction, etc. The annotated gene sets such as Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways represent knowledge about the functional association between genes, which may be of predictive value for disease gene identification. However, the knowledge is barely explored in existing approaches. In this study, we propose a new method for predicting disease-associated genes by integrating annotated gene sets. It first constructs a signal matrix by integrating annotated gene sets in the MSigDB data, a comprehensive database of curated gene sets. Then it uses the signal matrix as input features to build a prediction model with machine learning approaches. We compared our method with existing disease gene prediction methods on five complex diseases. The results showed that our method is superior to other methods.

关键词： Proteins Genomics Machine learning Predictive models Ontologies Prediction algorithms Graph neural networks

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Identification of Virus-Receptor Interactions Based on Network Enhancement and Similarity 16th

Identification of Virus-Receptor Interactions Based on Netwo...

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16th International Symposium on bioinformatics Research and Applications, ISBRA 2020

作者： Zhu, Lingzhi Yan, Cheng Duan, Guihua Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China School of Computer and Information Science Hunan Institute of Technology Hengyang421008 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China

ISBN: (纸本)9783030578206

As a main composition of the human-associated microbiome, viruses are directly associated with our health and disease. The receptor-binding is critical for the virus infection. So identifying potential virus-receptor interactions will help systematically understand the mechanisms of virus-receptor interactions and effectively treat infectious diseases caused by viruses. Several computational models have been developed to identify virus-receptor interactions based on assumption that similar viruses show similar interaction patterns with receptors and vice versa, but the performance need to be improved. Furthermore, the virus network and the receptor network are also noisy. Therefore, we present a new prediction model (NERLS) to identify potential virus-receptor interactions based on Network Enhancement, virus sequence information and receptor sequence information by Regularized Least Squares. Firstly, the virus network is constructed based on the virus sequence similarity and Gaussian interaction profile (GIP) kernel similarity of viruses by a mean method. They are calculated based on the viral RefSeq genomes downloaded from NCBI and known virus-receptor interactions, respectively. Similarly, we also use the same mean method to construct the receptor network based on the amino acid sequence similarity and known virus-receptor interactions. Then Network Enhancement is applied to denoise the virus network and the receptor network. Finally, we employ the regularized least squares algorithm to identify potential virus-receptor interactions. The 10-fold cross validation (10CV) experimental results indicate that an average Area Under Curve (AUC) values of NERLS is 0.8930, which is superior to other computing models of 0.8675 (IILLS), 0.7959 (BRWH), 0.7577 (LapRLS), and 0.7128 (CMF). Furthermore, the Leave One Out Cross Validation (LOOCV) experimental results also show that NERLS can achieve the AUC values of 0.9210, which is better than other models (IILLS: 0.9061, BRWH: 0

关键词： Viruses

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MTFIL-Net: automated Alzheimer’s disease detection and MMSE score prediction based on feature interactive learning

MTFIL-Net: automated Alzheimer’s disease detection and MMSE...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jin Liu Xu Tian Jianxin Wang Rui Guo Hulin Kuang Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Automatic detection of Alzheimer’s disease (AD) is conducive to intervention in the disease progression. MMSE score prediction can reveal the development of AD. In recent years, some studies have designed multi-task methods for AD detection and MMSE score prediction to take advantage of the correlation between them. However, how to use the correlation between the two task features is still a problem. To address this challenge, we propose a multi-task feature interactive leanrning network (MTFIL-Net) to perform AD detection and MMSE score prediction. First, we interact the features acquired by CNNs corresponding to the two tasks to take advantage of the feature correlation between the two tasks. The interaction module extracts the shared features of the two tasks and concatenate them with the features of the two task. Then, we design a joint loss based on cross entropy and smooth L1 function. We use the distribution of MMSE scores to dynamically adjust the relationship between the two tasks. We validate our method with subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We use the ADNI1 dataset for training and testing, and used the ADNI2 dataset as an external validation set. Our proposed MTFIL-Net reached an ACC of 0.86 for AD detection and a correlation coefficient of 0.67 for MMSE score prediction on the ADNI1 dataset, and reached an ACC of 0.85 for AD detection and a correlation coefficient of 0.66 for MMSE score prediction on the ADNI2 dataset. Experiment results show that MTFIL-Net effectively utilizes the correlation between AD and MMSE score.

关键词： Correlation coefficient Training Neuroimaging Correlation Feature extraction Multitasking Task analysis

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Collaborative Edge Service Placement for Maximizing QoS with Distributed Data Cleaning 31

Collaborative Edge Service Placement for Maximizing QoS with...

引用

31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Liang, Yuzhu Wang, Wenhua Zheng, Xi Liu, Qin Wang, Liang Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Computing Macquarie University NSW2109 Australia College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519000 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519000 China

ISBN: (纸本)9798350399738

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines. © 2023 IEEE.

关键词： Edge computing

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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De novo Prediction of Drug-Target Interaction via Laplacian Regularized Schatten-p Norm Minimization 1

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16th International Symposium on bioinformatics Research and Applications, ISBRA 2020

作者： Wu, Gaoyan Yang, Mengyun Li, Yaohang Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan Shaoyang University Shaoyang422000 China Department of Computer Science Old Dominion University NorfolkVA23529 United States

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

The identification of drug-target interactions plays a crucial role in drug discovery and design. However, capturing interactions between drugs and targets via traditional biochemical experiments is an extremely laborious, expensive and time-consuming procedure. Therefore, the use of computational methods for predicting potential interactions to guide the experimental verification has attracted a lot of attention. In this paper, we propose a new algorithm, named Laplacian Regularized Schatten-p Norm Minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets. First, we take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten-p norm minimization model to improve prediction performance in the new drug/target cases by combining the loss function with a Laplacian regularization term. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers (ADMM) algorithm. Performance evaluations on benchmark datasets show that LRSpNM achieves better and more robust performance than five state-of-the-art drug-target interaction prediction algorithms. In addition, we conduct case study in practical applications, which also illustrates the effectiveness of our proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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