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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Wu, Di Feng, Qilong Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Changsha 410205 China Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

ISBN: (纸本)9789819644445

The individual fairness k-median problem is a frequently encountered problem in applications involving center location, which generalizes the standard k-median problem by assigning each point a neighborhood radius, allowing connections only to centers within a constant factor of this radius. In this paper, we present a randomized polynomial-time approximation scheme (PTAS) framework with (2+O(ϵ))-fairness violation for the individual fairness k-median problem, improving upon the previous best approximation ratio of (7.081+ϵ) and fairness violation of 3. We propose a new dynamic programming approach to deal with the challenges caused by the individual fairness requirements, which is the crucial step in getting the improved ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： approximation algorithm clustering k-median

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Ten computational challenges in human virome studies

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Virologica Sinica 2024年第6期39卷 845-850页

作者： Yifan Wu Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha 410082China

In recent years,substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and *** this progress,the field of human virome research remains nascent,primarily hindered by the lack of effective methods,particularly in the domain of computational *** perspective systematically outlines ten computational challenges spanning various types of virome *** challenges arise due to the vast diversity of viromes,the absence of a universal marker gene in viral genomes,the low abundance of virus populations,the remote or minimal homology of viral proteins to known proteins,and the highly dynamic and heterogeneous nature of *** each computational challenge,we discuss the underlying reasons,current research progress,and potential *** resolution of these challenges necessitates ongoing collaboration among computational scientists,virologists,and multidisciplinary *** essence,this perspective serves as a comprehensive guide for directing computational efforts in human virome studies.

关键词： Human virome Computational biology Computational viromics Artificial intelligence

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Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms

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Virologica Sinica 2022年第3期37卷 437-444页

作者： Huiting Chen Zhaozhong Zhu Ye Qiu Xingyi Ge Heping Zheng Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha410082China

The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune ***,there is still a lack of effective tools for determining the cleavage sites of the 3CL *** study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and *** residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in *** RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus *** achieved an AUC of 0.95 and predicted correctly 80%of the cleavage ***,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF *** proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and ***,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF ***,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.

关键词： Coronavirus 3C-like protease Cleavage sites Machine-learning algorithms 3CLP webserver

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Computational Viromics: Applications of the Computational Biology in Viromics Studies

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Virologica Sinica 2021年第5期36卷 1256-1260页

作者： Congyu Lu Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha 410082China

Viruses are a kind of biological entities which rely on host cells for *** on the genetic materials and replication mode,they can be grouped into double-stranded DNA(dsDNA),single-stranded DNA(ssDNA),doublestranded RNA(dsRNA),positive-sense single-stranded RNA(+ssRNA),negative-sense single-stranded RNA(-ssRNA),ssRNA reverse transcriptase viruses(ssRNART)and dsDNA reverse transcriptase viruses(dsDNA-RT)(Walker et al.2020).Viruses can infect most kinds of biological entities,including viruses,bacteria,archaea and eukaryote(La Scola et al.2008;Fermin,2018).

关键词： al. dsDNA stranded

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Evaluating the performance of the PREDAC method in flu vaccine recommendations over the past decade (2013-2023)

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Virologica Sinica 2025年第2期40卷 288-291页

作者： Yousong Peng Lei Yang Weijuan Huang Mi Liu Xiao Ding Xiangjun Du Yuelong Shu Taijiao Jiang Dayan Wang Bioinformatics Center College of Biology Hunan Provincial Key Laboratory of Medical Virology Hunan University Changsha 410082 China. National Institute for Viral Disease Control and Prevention China CDC Beijing 102206 China. Jiangsu Institute of Clinical Immunology The First Affiliated Hospital of Soochow University Suzhou 215006 China. Suzhou Institute of Systems Medicine Suzhou 215123 China. Department of Biostatistics and Systems Biology School of Public Health (Shenzhen) Sun Yat-sen University Shenzhen Guangdong 510275 China. Key Laboratory of Pathogen Infection Prevention and Control (MOE) State Key Laboratory of Respiratory Health and Multimorbidity National Institute of Pathogen Biology Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 102629 China. Electronic address: yshu@***. Guangzhou National Laboratory Guangzhou 510005 China. Electronic address: jiang_taijiao@***. National Institute for Viral Disease Control and Prevention China CDC Beijing 102206 China. Electronic address: dayanwang@***.

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EVIHVR: A platform for analysis of expression, variation and identification of human virus receptors

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Infectious Medicine 2022年第1期1卷 59-62页

作者： Zheng Zhang Zena Cai Longfei Mao Xing-Yi Ge Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangshaChina

Motivation:Virus receptors are presented on the cell surfaces of a host and are key for viral infection of host ***,no unified resource for the study of viral receptors is currently ***:To address this problem,we built EVIHVR,a platform for analyzing the expression and variation,and for the identification of human virus *** provides three functions:(1)Receptor expression function for browsing and analyzing the expression of human virus receptors in various human tissues/cells;(2)Receptor gene polymorphism function for analyzing the genetic polymorphism of human virus receptors in different human populations and human tissues;and(3)Predict receptor function for identifying potential virus receptors based on differential expression *** can become a useful tool for the analysis and identification of human virus receptors.

关键词： Virus receptor Genetic polymorphism Receptor identification Human-infecting virus Web services

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Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction

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Journal of Computer Science & Technology 2021年第2期36卷 310-322页

作者： Li-Gang Gao Meng-Yun Yang Jian-Xin Wang School of Computer Science and Engineering Central South UniversityChangsha 410083China Hunan Provincial Key Laboratory of Bioinformatics Central South UniversityChangsha 410083China School of Science Shaoyang UniversityShaoyang 422000China

Identifying the potential drug-target interactions(DTI)is critical in drug *** drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction ***,many corresponding models cannot accurately express the relationship between similarity features and DTI *** order to rationally represent the correlation,we propose a novel matrix factorization method,so-called collaborative matrix factorization with soft regularization(SRCMF).SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix *** contrast to general collaborative matrix factorization,the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate,not ***,SRCMF obtains low-rank feature representations of drug similarity and target similarity,and then uses a soft regularization term to constrain the approximation between drug(target)similarity features and drug(target)potential features of *** comprehensively evaluate the prediction performance of SRCMF,we conduct cross-validation experiments under three different *** terms of the area under the precision-recall curve(AUPR),SRCMF achieves better prediction results than six state-of-the-art ***,under different noise levels of similarity data,the prediction performance of SRCMF is much better than that of collaborative matrix *** conclusion,SRCMF is robust leading to performance improvement in drug-target interaction prediction.

关键词： drug-target interaction collaborative matrix factorization soft regularization noisy data

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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 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 Changsha 410013 Hunan PR China

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. Conclusion 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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Prediction of the Receptorome for the Human-Infecting Virome

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Virologica Sinica 2021年第1期36卷 133-140页

作者： Zheng Zhang Sifan Ye Aiping Wu Taijiao Jiang Yousong Peng Bioinformatics Center of College of Biology Hunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha 410082China Center for Systems Medicine Institute of Basic Medical SciencesChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100005China Suzhou Institute of Systems Medicine Suzhou 215123China Guangzhou Regenerative Medicine and Health Guangdong Laboratory Guangzhou 510005China

The virus receptors are key for the viral infection of host *** of the virus receptors is still challenging at *** previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level,high number of interaction partners and high expression ***,a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences.A total of 1424 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting *** addition,the combination of the random-forest model with protein–protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses,such as the enterovirus,norovirus and West Nile ***,the candidate alternative receptors of the SARS-Co V-2 were also predicted in this *** far as we know,this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.

关键词： Receptor Human-infecting virus Virome bioinformatics Emerging virus

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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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