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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Lingzhi Zhu Kai Zheng Guihua Duan Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China School of Computer Science and Engineering Hunan Institute of Technology Hengyang Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Receptor-binding is the first step of viral infection. Discovering potential virus-receptor interactions may give insight into potential strategies for treating viral infectious diseases. Most of computational methods for the virus-receptor interaction prediction are mainly based on sequence information. They neither makes effective use of structure information nor effectively handles with missing values of multiple similarities. In addition, the Link Prediction via linear optimization (LP) only uses contribution of neighbors of a node and ignores contribution of neighbors of another node on the network link. In this article, we present a virus-receptor interaction prediction method (MVLP) based on Multi-View learning and LP via contributions of all neighbors of two nodes on the network link. First, missing values of the receptor secondary structure similarity, the receptor conserved domain secondary structure similarity, the viral protein secondary structure similarity, the viral protein sequence similarity and the viral genome sequence similarity are updated by the gaussian radial basis function (GRB). To improve these similarities, we fuse updated and initial values of each similarity with multi-view learning, respectively. Next, three virus values and receptor similarities are integrated into the comprehensive virus and receptor similarity by the averaging method, respectively. Finally, LP based on contribution of neighbors of two nodes is presented for the virus-receptor interaction prediction. To evaluate the ability of MVLP, we compare MVLP with four related methods in 10 fold Cross-Validation (10CV). Computational results indicate that an average Area Under Curve (AUC) values of MVLP on viralReceptor sup and viralReceptor are 0.9427 and 0.9444, respectively, which are superior to other related methods. Furthermore, a case study also demonstrates the ability of MVLP in practice.

关键词： Protein engineering computer viruses Infectious diseases Fuses Genomics Prediction methods Protein sequence

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Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to K-Level Stochastic Optimizations 41

Stability and Generalization for Stochastic Recursive Moment...

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41st International Conference on Machine Learning, ICML 2024

作者： Pan, Xiaokang Li, Xingyu Liu, Jin Sun, Tao Sun, Kai Chen, Lixing Qu, Zhe Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Department of Computer Science Tulane University New Orleans United States College of Computer National University of Defense Technology Changsha China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Institute of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China

STOchastic Recursive Momentum (STORM)based algorithms have been widely developed to solve one to K-level (K ≥ 3) stochastic optimization problems. Specifically, they use estimators to mitigate the biased gradient issue and achieve near-optimal convergence results. However, there is relatively little work on understanding their generalization performance, particularly evident during the transition from one to K-level optimization contexts. This paper provides a comprehensive generalization analysis of three representative STORM-based algorithms: STORM, COVER, and SVMR, for one, two, and K-level stochastic optimizations under both convex and strongly convex settings based on algorithmic stability. Firstly, we define stability for K-level optimizations and link it to generalization. Then, we detail the stability results for three prominent STORM-based algorithms. Finally, we derive their excess risk bounds by balancing stability results with optimization errors. Our theoretical results provide strong evidence to complete STORM-based algorithms: (1) Each estimator may decrease their stability due to variance with its estimation target. (2) Every additional level might escalate the generalization error, influenced by the stability and the variance between its cumulative stochastic gradient and the true gradient. (3) Increasing the batch size for the initial computation of estimators presents a favorable trade-off, enhancing the generalization performance. Copyright 2024 by the author(s)

关键词： Optimization algorithms

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Deep learning Radiopathomics based on pretreatment MRI and whole slide images for predicting overall survival in locally advanced nasopharyngeal carcinoma

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Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2025年 209卷 110949页

作者： Xiaochun Yi Xiaoping Yu Congrui Li Junjian Li Hui Cao Qiang Lu Junjun Li Jing Hou Department of Diagnostic Radiology Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Changsha 410013 Hunan PR China. Hunan Provincial Key Laboratory on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 PR China. Department of Health Service Center Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Changsha 410013 Hunan PR China. Department of Pathology Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University 283 Tongzipo Road Yuelu District Changsha Hunan 410013 PR China. Department of Diagnostic Radiology Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Changsha 410013 Hunan PR China. Electronic address: houjing@***.

PURPOSE:To develop an integrative radiopathomic model based on deep learning to predict overall survival (OS) in locally advanced nasopharyngeal carcinoma (LANPC) patients. MATERIALS AND METHODS:A cohort of 343 LANPC patients with pretreatment MRI and whole slide image (WSI) were randomly divided into training (n = 202), validation (n = 91), and external test (n = 50) sets. For WSIs, a self-attention mechanism was employed to assess the significance of different patches for the prognostic task, aggregating them into a WSI-level representation. For MRI, a multilayer perceptron was used to encode the extracted radiomic features, resulting in an MRI-level representation. These were combined in a multimodal fusion model to produce prognostic predictions. Model performances were evaluated using the concordance index (C-index), and Kaplan-Meier curves were employed for risk stratification. To enhance model interpretability, attention-based and Integrated Gradients techniques were applied to explain how WSIs and MRI features contribute to prognosis predictions. RESULTS:The radiopathomics model achieved high predictive accuracy in predicting the OS, with a C-index of 0.755 (95 % CI: 0.673-0.838) and 0.744 (95 % CI: 0.623-0.808) in the training and validation sets, respectively, outperforming single-modality models (radiomic signature: 0.636, 95 % CI: 0.584-0.688; deep pathomic signature: 0.736, 95 % CI: 0.684-0.810). In the external test, similar findings were observed for the predictive performance of the radiopathomics, radiomic signature, and deep pathomic signature, with their C-indices being 0.735, 0.626, and 0.660 respectively. The radiopathomics model effectively stratified patients into high- and low-risk groups (P < 0.001). Additionally, attention heatmaps revealed that high-attention regions corresponded with tumor areas in both risk groups. CONCLUSIO:n: The radiopathomics model holds promise for predicting clinical outcomes in LANPC patients, offering a potential

关键词： Deep learning Digital pathology Magnetic resonance imaging Nasopharyngeal carcinoma Prognosis Risk stratification

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Research on Prediction Methods of Prevalence Perception under Information Exposure

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computers, Materials & Continua 2020年第12期65卷 2263-2275页

作者： Weijin Jiang Fang Ye Wei Liu Xiaoliang Liu Guo Liang Yuhui Xu Lina Tan School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha410205China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Mobile E-Business Collaborative Innovation Center of Hunan ProvinceHunan University of Technology and BusinessChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China School of Bioinformatics University of MinnesotaTwin CitiesUSA

With the rapid development of information technology,the explosive growth of data information has become a common challenge and *** network services represented by WeChat,Weibo and Twitter,drive a large amount of information due to the continuous spread,evolution and emergence of users through these *** dynamic modeling,analysis,and network information prediction,has very important research and application value,and plays a very important role in the discovery of popular events,personalized information recommendation,and early warning of bad *** these reasons,this paper proposes an adaptive prediction algorithm for network information transmission.A popularity prediction algorithm is designed to control the transmission trend based on the gray Verhulst model to analyze the law of development and capture popular *** simulations show that the proposed perceptual prediction model in this paper has a better fitting effect than the existing models.

关键词： Social network situational awareness adaptive prediction prediction of popularity

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The Fair k-Center with Outliers Problem: FPT and Polynomial Approximations 1

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17th International Joint Conference on Theoretical computer science - Frontier of Algorithmic Wisdom, IJTCS-FAW 2023

作者： Wu, Xiaoliang Feng, Qilong Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Engineering State University of New York at Buffalo New York14260-1660 United States Hunan Provincial Key Lab on Bioinformatics Central South University Changsha410083 China

ISBN: (数字)9783031393440

ISBN: (纸本)9783031393433

The fair k-center and k-center with outliers problems are two important variants of the k-center problem in computer science, which have attracted lots of attention. The previous best results for the above two problems are a 3-approximation (ICML 2020) and a 2-approximation (ICALP 2016), respectively. In this paper, we consider a common generalization of the two mentioned variants of the k-center problem, denoted as the fair k-center with outliers (FkCO) problem. For the FkCO problem, we are given a set X of points in a metric space and parameters k and z, where the points in X are divided into several groups, and each point is assigned a color to denote which group it belongs to. The goal is to find a subset C⊆ X of k centers and a set Z of at most z outliers such that C satisfies fairness constraints, and the objective maxx∈X\Zminc∈Cd(x, c) is minimized. In this paper, we study the Fixed-Parameter Tractability (FPT) approximation algorithm and polynomial approximation algorithm for the FkCO problem. Our main contributions are: (1) we propose a (1 + ϵ) -approximation algorithm in FPT time for the FkCO problem in a low-dimensional doubling metric space;(2) we achieve a polynomial 3-approximation algorithm for the FkCO problem with the reasonable assumptions that all optimal clusters are well separated and have size greater than z. © 2023, Springer Nature Switzerland AG.

关键词： Approximation algorithms

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MiRNA-Disease Associations Prediction Based on Negative Sample Selection and Multi-layer Perceptron 1

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

作者： Li, Na Duan, Guihua Yan, Cheng Wu, Fang-Xiang Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China School of Computer and Information Qiannan Normal University for Nationalities DuyunGuizhou558000 China Division of Biomedical Engineering and Department of Mechanical Engineering University of Saskatchewan SaskatoonSKS7N5A9 Canada

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

MicroRNAs (miRNAs) are a class of non-coding RNAs of approximately 22 nucleotides. Cumulative evidence from biological experiments has confirmed that miRNAs play a key role in many complex human diseases. Therefore, the accurate identification of potential associations between miRNAs and diseases is beneficial to understanding the mechanisms of diseases, developing drugs and treating complex diseases. We propose a new method to predict miRNA-disease associations based on a negative sample selection strategy and multi-layer perceptron (called NMLPMDA). For obtaining more similarity information, NMLPMDA integrates the miRNA functional similarity and the Gaussian interaction profile (GIP) kernel similarity of miRNAs as the final miRNA similarity, and integrates the disease semantic similarity and the GIP kernel similarity of diseases as the final disease similarity. In particular, we propose a negative sample selection strategy based on common gene information to select more reliable negative samples from unknown miRNA-disease associations. The 5-fold cross validation is used to evaluate the performance of NMLPMDA and other competing methods. On four datasets (HMDD2.0-Yan, HMDD2.0-Lan, HMDD2.0-You, HMDD3.0), the AUC values of NMLPMDA are 0.9278, 0.9206, 0.9301 and 0.9350, respectively. In addition, we also illustrate the prediction ability of NMLPMDA in Lymphoma. As a result, 28 of the top 30 miRNAs associated with the disease have been validated experimentally in dbDEMC and previous studies, respectively. These experimental results indicate that NMLPMDA is a reliable model for predicting associations between miRNAs and diseases. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Machine learning

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Personalized federated adaptive regularization for heterogeneous medical image classification: COVID-19 CT results

Personalized federated adaptive regularization for heterogen...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Yuan Liu Zhe Qu Shu Wang Tao Sheng Xiaoqing Yu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Bank Of Sanxiang Changsha Hunan China

During outbreaks of large infectious diseases like COVID-19, there is a strain on healthcare resources worldwide. To alleviate the burden on healthcare workers during the initial stages of the outbreak, there is an urgent need for the development of automated tools. Federated learning offers a privacypreserving solution to the challenge that limited annotated data within single healthcare facilities. However, existing federated aggregation strategies cannot adapt to the real-world medical image data problem of mixed heterogeneous. This paper introduces Personalized Federated Adaptive Regularization (pFedAR), an adaptive framework for federated learning that effectively utilizes multi-site COVID-19 CT datasets with mixed heterogeneous of distribution discrepancies. To solve the mixed distribution of label skew and feature shift, we propose a two-stage adaptive regularization. In the client training stage, we use the balance loss term to balance the COVID-19 clients with missing label. In the aggregation stage, we correct the client gradient conflict. Our method is developed and evaluated using eight real-world COVID-19 diagnosis datasets composed of CT images. Extensive experiments demonstrate the consistent improvement achieved by our method across the datasets.

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Enterprises application integration framework based on web services and its interfaces

Enterprises application integration framework based on web s...

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International Conference on computer, Informatics, Cybernetics and Applications 2011, CICA 2011

作者： Li, Zhangbing Che, Wujiang Liu, Jianxun School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Lab. of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9789400718388

Traditional technologies for enterprise applications integration (EAI), such as CORBA and EJB/DCOM component or middleware, can not be handily extended in the systems for dynamic business requirements. To solve the problem, using the principle of service-oriented architecture (SOA), an EAI framework based on web services and its interfaces are designed. The framework is extensible to dynamic business requirement, and achieves well heterogeneous datum sharing and integration of application systems in enterprises. © 2012 Springer science+Business Media B.V.

关键词： Web services

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Linear Time Approximation Algorithm for Column Subset Selection with Local Search 38

Linear Time Approximation Algorithm for Column Subset Select...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Yuanbin Huang, Ziyun Xu, Jinhui Wang, Jianxin Feng, Qilong School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d input matrix A that minimizes the residual error A − SS†A2F, where S† is the Moore-Penrose inverse matrix of S. Many previous approximation algorithms have non-linear running times in both n and d, while the existing linear-time algorithms have a relatively larger approximation ratios. Additionally, the local search algorithms in existing results for solving the CSS problem are heuristic. To achieve linear running time while maintaining better approximation using a local search strategy, we propose a local search-based approximation algorithm for the CSS problem with exactly k columns selected. A key challenge in achieving linear running time with the local search strategy is how to avoid exhaustive enumerations of candidate columns for constructing swap pairs in each local search step. To address this issue, we propose a two-step mixed sampling method that reduces the number of enumerations for swap pair construction from O(dk) to k in linear time. Although the two-step mixed sampling method reduces the search space of local search strategy, bounding the residual error after swaps is a non-trivial task. To estimate the changes in residual error after swaps, we propose a matched swap pair construction method to bound the approximation loss, ensuring a constant probability of loss reduction in each local search step. In expectation, these techniques enable us to obtain the local search algorithm for the CSS problem with theoretical guarantees, where a 53(k + 1)-approximate solution can be obtained in linear running time O(ndk4 log k). Empirical experiments show that our proposed algorithm achieves better quality and time compared to previous algorithms on both small and large datasets. Moreover, it is at least 10 times faster than state-of-the-art algorithms a

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