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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Correntropy based self-organizing map

Correntropy based self-organizing map

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2016 International Conference on machine learning and Cybernetics, ICMLC 2016

作者： Shang, Qing-Zhen Xing, Hong-Jie Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China

ISBN: (纸本)9781509003891

Self-organizing map (SOM) is regarded as a type of feedfoward neural network. It has been successfully used for unsupervised learning. However, the objective function of the traditional SOM relies on the mean squared error (MSE) criterion, which makes the performance of SOM become poor in the presence of noise. In the paper, correntropy based measure is proposed to substitute MSE to enhance the anti-noise ability of SOM. Moreover, the half-quadratic optimization technique is utilized to deal with the corresponding optimization problem. Experimental results demonstrate that the proposed method achieves better anti-noise performance in comparison with the traditional SOM. © 2016 IEEE.

关键词： Self organizing maps

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Correntropy based self-organizing map

Correntropy based self-organizing map

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Qing-Zhen Shang Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Province China

ISBN: (纸本)9781509003914

关键词： Neurons Distributed databases Optimization Topology Kernel Cybernetics Self-organizing feature maps

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A fast positive-region reduction method based on dominance-equivalence relations

A fast positive-region reduction method based on dominance-e...

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2016 International Conference on machine learning and Cybernetics, ICMLC 2016

作者： Jin, Yongfei Li, Yan He, Qiang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China School of Science Beijing University of Civil Engineering and Architecture Beijing102616 China

ISBN: (纸本)9781509003891

In this paper, we consider decision systems which consist of preference ordered conditional attributes and symbolic decision attributes. Thus, dominance relations and equivalence relations can be respectively defined on the conditional attribute set and decision attribute set. Based on this combined dominance-equivalence relations, a fast positive-region reduction (FPR) algorithm is developed by introducing the concept of positive approximation together with positive region reduction. The computation load of attribute reduction has been greatly reduced when compared with the original positive-region reduction algorithm (PR). Finally, the experimental results on 9 UCI data are demonstrated to make comparisons between the PR and the FPR method. © 2016 IEEE.

关键词： Approximation algorithms

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An improved hybrid model for order quantity allocation and supplier risk exposure

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International Journal of Fuzzy System Applications 2016年第3期5卷 120-147页

作者： Ng, Peh Sang Zhang, Feng Department of Physical and Mathematical Science Universiti Tunku Abdul Rahman Jalan Universiti Perak Malaysia Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Hebei China

This paper investigates the risk exposure arising from the supplier evaluation criteria of cost, quality, delivery, and flexibility. An integrated method of Fuzzy Decision Making Trial and Evaluation laboratory (FDEMATEL) and Fuzzy Analytical Network Process (FANP), which is able to address interaction and feedback effects between the criteria and subjectivity in the decision rating, is used to generate the importance weights of the objectives for a network relationship problem. Different weights of the objectives are then incorporated into a Fuzzy Multi-Objective Programming (FMOP) model for determining the optimal order quantity allocated to the suppliers. The proposed method provides a more realistic decision making scenario by considering different importance weights of criteria from individual priority. Therefore, it provides a practical method to solve the real world problems. The validity of the proposed method is illustrated by a case study from a trading company. © 2016, IGI Global.

关键词： Risk assessment

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Index construction for mathematical expression retrieval based on Trie tree 4th

Index construction for mathematical expression retrieval bas...

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4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Sun, Jing Tian, Xuedong Liu, Dazhong Zhang, Zhiming School of Computer Science and Technology Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9781138028302

With two-dimensional structures, mathematical expressions deliver more information than normal text not only with symbols but also their spatial arrangements. Users of mathematical retrieval systems need more search modes, such as fuzzy matching mode, rather than only exact matching mode to fit the features of mathematical expressions. Therefore, more and more researchers focus on how to retrieve mathematical expressions on the levels of structural similarity matching and synonyms matching. In this paper, an index structure based on Trie tree is designed oriented to measure the structural similarity of mathematical expressions. Firstly, the existing math indexing and matching method are introduced. Then, a stack algorithm of extracting information of math operators in LaTeX format is given. Finally, an index method based on Trie Tree is proposed for structural matching of mathematical expressions. The experimental analysis shows the effectiveness of the index method in this paper. © 2015 Taylor & Francis Group, London.

关键词： Search engines

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Selective ensemble of RBFNNs based on improved negative correlation learning 13

Selective ensemble of RBFNNs based on improved negative corr...

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13th International Conference on machine learning and Cybernetics, ICMLC 2014

作者： Xing, Hongjie Liu, Lifei Li, Sen Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei Province071002 China

ISBN: (纸本)9783662456514

In this paper, a novel selective ensemble method based on the improved negative correlation learning is proposed. To make the proposed ensemble strategy more robust against noise, correntropy is utilized to substitute mean square error (MSE). Moreover, an L1-norm based regularization term of ensemble weights is incorporated into the objective function of the proposed ensemble strategy to fulfill the task of selective ensemble. The half-quadratic optimization technique and the surrogate function method are used to solve the optimization problem of the proposed ensemble strategy. Experimental results on two synthetic data sets and the five benchmark data sets demonstrate that the proposed method is superior to the single radial basis function neural network (RBFNN). © Springer-Verlag Berlin Heidelberg 2014.

关键词： Mean square error

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Robust smooth one-class support vector machine 2

Robust smooth one-class support vector machine

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2nd International Conference on Information Technology and Electronic Commerce, ICITEC 2014

作者： Hu, Jin-Kou Xing, Hong-Jie Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China

ISBN: (纸本)9781479952984

In this paper, a novel one-class classification approach, namely, robust smooth one-class support vector machine (RSOCSVM) is proposed. The proposed method can efficiently enhance the anti-noise ability of the traditional one-class support vector machine (OCSVM). Utilizing the smooth technique, RSOCSVM reformulates the quadratic programming problem of OCSVM as an unstrained optimization format. Moreover, half-quadratic minimization is used to solve the obtained unstrained optimization problem. Experimental results on two synthetic data sets and nine benchmark data sets demonstrate that the proposed method is superior to the traditional OCSVM. © 2014 IEEE.

关键词： Support vector machines

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Linear discriminant analysis based on Zp-norm maximization 2

Linear discriminant analysis based on Zp-norm maximization

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2nd International Conference on Information Technology and Electronic Commerce, ICITEC 2014

作者： An, Lei-Lei Xing, Hong-Jie Key Laboratory of Machine Learning and Computational Intelligence College of Computer Science and Technology Hebei University Baoding Hebei Province071002 China

ISBN: (纸本)9781479952984

In this paper, linear discriminant analysis (LDA) based on Lp-norm (LDA-Lp) optimization method is proposed. The objective function utilizing the Lp-norm with arbitrary p value is studied. By maximizing the Lp-norm-based ratio between the between-class scatter and the within-class scatter, LDA-Lp can construct a set of local optimal projection vectors. Moreover, the optimal projection vectors can be obtained by the gradient ascent method. Experimental results on the two synthetic and fourteen benchmark datasets demonstrate that the better performance of LDA-Lp can be achieved by choosing the optimal value of p. © 2014 IEEE.

关键词： Benchmarking

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An improved approach to ordinal classification 13

An improved approach to ordinal classification

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13th International Conference on machine learning and Cybernetics, ICMLC 2014

作者： Wang, Donghui Zhai, Junhai Zhu, Hong Wang, Xizhao Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071002 China

ISBN: (纸本)9783662456514

A simple ordinal classification approach (SOCA) has been proposed by Frank and Hall. SOCA is a general method, any classification algorithm such as C4.5, k nearest neighbors (KNN) algorithm and extreme learning machine (ELM) etc. can be applied to this approach. We find that in SOCA only ordering information of decision attribute is used to classify objects but the ordering information of conditional attributes is not considered. Furthermore we experimentally find that ordering information of conditional attributes can also improve the generalization ability of the classification method. In this paper, we propose an improved ordinal classification methodology by employing the ordering information of both condition and decision attributes. In addition, we analyze the sensitivity of the SOCA on performance to the underlying classification algorithms, for instance, C4.5, KNN and ELM. A number of experiments are conducted and the experimental results show that the proposed method is feasible and effective. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Decision trees

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