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Fuzzy Preference Completion with Ranked and Unranked Preferences

Cognitive Computation 2025年第3期17卷 1-21页

作者： Li, Lei Liu, Pan Zhang, Renjie Tao, Zhenchao Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Radiation Oncology The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Department of Radiation Oncology Anhui Provincial Cancer Hospital Hefei China

As for social choice, all alternatives are ranked by agents to form preferences as linear orders. However, in applications, sometimes some alternatives cannot be ranked, or it is unnecessary to rank them, which leads to unranked alternatives. Hence, without loss of generality, by dividing the set of alternatives into three ranked and unranked subsets, including top-k alternatives, intermediate-r alternatives, and last-l alternatives, the Mallows model on ranked and unranked preferences can be analyzed systematically. Technically, a repeated insertion model is adopted during sampling, and probability distributions are derived for ranked and unranked preferences of alternatives. Experimental results verify the accuracy of the probability distributions for different ranked and unranked preferences of alternatives. Furthermore, in order to solve the preference completion problem where agents have multiple partial rankings, a fuzzy preference completion algorithm, Fuzzy-Multi-Rankings, is proposed, which introduces a fuzzy ranking to complete the target agent’s preference in addition to the traditional nearest-neighbor-based methods. Based on the three ranked and unranked preferences, seven cases can be classified and analyzed for fuzzy preference completion. Experiments on the synthetic datasets and MovieLens dataset confirm the effectiveness and efficiency of our proposed Fuzzy-Multi-Rankings algorithm and also verify the accuracy of the evaluated probability distributions for the proposed seven cases.

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Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data

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Intelligent Oncology 2025年第1期1卷 17-30页

作者： Yang Luo Yilin Li Mengjie Fang Shuo Wang Lizhi Shao Ruiyang Zou Di Dong Zhenyu Liu Jingwei Wei Jie Tian Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing 100190 China Beijing Key Laboratory of Molecular Imaging Beijing 100190 China School of Life Science and Technology Xidian University Xi’an Shaanxi 710126 China School of Public Health Anhui Medical University Hefei Anhui 230032 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing 100191 China School of Engineering Medicine Beihang University Beijing 100191 China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology Beihang University Beijing 100191 China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi’an Shaanxi 710126 China

The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological data. This review aims to explore the synergy between three key omics domains: radiomics, genoproteomics, and pathomics. Radiomics involves extracting high-dimensional data from medical images, providing valuable insights into tumor heterogeneity and treatment response. Genoproteomics, encompassing both genomic and proteomic analyses, delves into the molecular mechanisms driving cancer progression and therapeutic resistance. Pathomics leverages advanced digital pathology techniques to quantitatively analyze tissue architecture and cellular morphology. We provide an in-depth overview of the methodologies and tools employed in each omics field, highlighting their specific applications in oncology, including cancer diagnosis, biomarker discovery, and prediction of treatment outcomes. Furthermore, we discuss the integration of multi-omics data, addressing the challenges and innovative solutions for harmonizing these complex datasets. Through an examination of recent advancements and case studies, we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.

关键词： Oncology Multi-omics Radiomics Genoproteomics Pathomics

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HiCur-NPC: Hierarchical Feature Fusion Curriculum Learning for Multi-Modal Foundation Model in Nasopharyngeal Carcinoma

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IEEE Transactions on Medical Imaging 2025年

作者： Wang, Zipei Fang, Mengjie Tang, Linglong Tian, Jie Dong, Di Chinese Academy of Sciences CAS Key Laboratory of Molecular Imaging Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China Beihang University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beijing100191 China Beihang University Ministry of Industry and Information Technology Key Laboratory of Big Data-Based Precision Medicine Beijing100191 China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Collaborative Innovation Center for Cancer Medicine Department of Radiation Oncology Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Guangzhou China

Providing precise and comprehensive diagnostic information to clinicians is crucial for improving the treatment and prognosis of nasopharyngeal carcinoma. Multi-modal foundation models, which can integrate data from various sources, have the potential to significantly enhance clinical assistance. However, several challenges remain: (1) the lack of large-scale visual-language datasets for nasopharyngeal carcinoma;(2) the inability of existing pre-training and fine-tuning methods to capture the hierarchical features required for complex clinical tasks;(3) current foundation models having limited visual perception due to inadequate integration of multi-modal information. While curriculum learning can improve a model’s ability to handle multiple tasks through systematic knowledge accumulation, it still lacks consideration for hierarchical features and their dependencies, affecting knowledge gains. To address these issues, we propose the Hierarchical Feature Fusion Curriculum Learning method, which consists of three stages: visual knowledge learning, coarse-grained alignment, and fine-grained fusion. First, we introduce the Hybrid Contrastive Masked Autoencoder to pre-train visual encoders on 755K multi-modal images of nasopharyngeal carcinoma CT, MRI, and endoscopy to fully extract deep visual information. Then, we construct a 65K visual instruction fine-tuning dataset based on open-source data and clinician diagnostic reports, achieving coarse-grained alignment with visual information in a large language model. Finally, we design a Mixture of Experts Cross Attention structure for deep fine-grained fusion of global multi-modal information. Our model outperforms previously developed specialized models in all key clinical tasks for nasopharyngeal carcinoma, including diagnosis, report generation, tumor segmentation, and prognosis. © 1982-2012 IEEE.

关键词： Curricula

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An artificial intelligence and blockchain technology-based data management framework for multicenter randomized controlled trials

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Science Bulletin 2025年第6期70卷 856-860页

作者： Dongyuan Yun Xiaohang Wu Xi Chen Yahan Yang Yuanjun Shang Shaopeng Liu Dinesh Visva Gunasekeran Duoru Lin Lixue Liu Lanqin Zhao Wenben Chen Jingjing Chen Ling Jin Yuxuan Wu Jiaming Hong Weiling Hu Zhenzhe Lin Carol Y.Cheung Xiang Chen Peichen Xie Zhenzhen Liu Changhai Ding Patrick Yu-Wai-Man Keming Yu Daniel Shu Wei Ting Yizhi Liu Zibin Zheng Gansen Zhao Zhihua Xia Tien Yin Wong Haotian Lin on behalf of Advanced Technologies Improve Clinical Trial(ATICT)Group State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510000China School of Biomedical Engineering Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510000China Singapore Eye Research Institute Singapore National Eye CenterSingapore999002Singapore School of Medical Information Engineering Guangzhou University of Chinese MedicineGuangzhou 510000China Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhou 510000China Clinical Research Center Zhujiang HospitalSouthern Medical UniversityGuangzhou 510000China Cambridge Center for Brain Repair and MRC Mitochondrial Biology Unit Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCB20XYUnited Kingdom UCL Institute of Ophthalmology University College LondonLondonWC1E 6BTUnited Kingdom School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science South China Normal UniversityGuangzhou 510000China College of Cyber Security Jinan UniversityGuangzhou 510000China Duke-NUS Medical School Singapore999002Singapore Tsinghua Medicine Tsinghua UniversityBeijing 100010China Hainan Eye Hospital and Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityHaikou 570100China Centre for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhou 510000China 不详

Randomized controlled trials(RCTs)provide optimal evidence of the effectiveness and safety of a new drug,a new medical device,or a new therapeutic strategy with the necessary scientific design[1].Traditional electronic data collection(EDC)systems for clinical trials primarily focus on data entry,validation,and compliance ***,their reliance on centralized architectures introduces vulnerabilities in data security and integrity。

关键词： Artificial Intelligence Multicenter Randomized Controlled Trials data Security scientific design traditional Blockchain Technology clinical trials randomized controlled trials rcts provide centralized architectures

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Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation

arXiv

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arXiv 2025年

作者： Lyu, Shen-Huan He, Yi-Xiao Wang, Yanyan Qu, Zhihao Tang, Bin Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China

Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been proven to have a stronger representation ability than DT, as it provides a way to create shallower tree structures while still approximating complex decision boundaries. However, its learning efficiency is still insufficient, since the linear projections cannot be transmitted to the child nodes, resulting in a waste of model parameters. In this work, we propose an enhanced ODT method with Feature Concatenation (FC-ODT), which enables in-model feature transformation to transmit the projections along the decision paths. Theoretically, we prove that our method enjoys a faster consistency rate w.r.t. the tree depth, indicating that our method possesses a significant advantage in generalization performance, especially for shallow trees. Experiments show that FC-ODT can outperform the other state-of-the-art decision trees with a limited tree depth. Copyright © 2025, The Authors. All rights reserved.

关键词： Efficiency

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

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IEEE International Symposium on Biomedical Imaging

作者： Lidan Fu Binchun Lu Lingbing Li Xiaojing Shi Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000–1300 nm). NIR-IIb (1500–1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice.

关键词： Training Image resolution Explainable AI Statistical analysis Noise Imaging Fluorescence Generative adversarial networks Real-time systems Probes

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Recent advances in intelligent sports based on triboelectric nanogenerators

Intelligent Sports and Health

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Intelligent Sports and Health 2025年第2期1卷 51-66页

作者： Lijun Huang Jiamin Wu Huaihong Cai Minglan Ji Yaokun Pang Jianjun Luo Haibo Zhou Zhong Lin Wang Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 PR China School of Nanoscience and Engineering University of Chinese Academy of Sciences Beijing 100049 PR China Guangdong Second Provincial General Hospital Postdoctoral Research Station of Traditional Chinese Medicine College of Pharmacy Jinan University Guangzhou 510632 PR China College of Chemistry and Materials Science Jinan University Guangzhou 510632 PR China State Key Laboratory of Bio-Fibers and Eco-Textiles College of Materials Science and Engineering Qingdao University Qingdao 266071 PR China Guangzhou Institute of Blue Energy Knowledge City Huangpu District Guangzhou 510555 PR China

The development of emerging technologies such as Internet of Things, big data analysis, and artificial intelligence (AI) has made athlete digitalization crucial in competitive sports. Triboelectric nanogenerator (TENG) is an emerging technology for mechanical energy harvesting and self-powered sensing, which offers significant potential in intelligent sports by reducing battery dependency. This paper reviews the recent advancements in TENG applications for intelligent sports, toward the future application of artificial intelligence for sports. Firstly, the working mechanism of TENG and its relationship with big data in sports are introduced. Subsequently, the focus shifts to the applications of TENGs in sports facilities, wearable equipment, and medical health devices. In addition, the integration of TENG with hybrid nanogenerators is explored for multi-functional and precise motion sensing. Finally, the challenges and future prospects of TENGs in intelligent sports are also discussed.

关键词： Triboelectric nanogenerator Intelligent sports Energy harvesting Self-powered system Self-powered sensing Wearable electronics

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RJMIM: A new feature selection method based on joint mutual information

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Revista de la Facultad de Ingenieria 2016年第4期31卷 59-69页

作者： Peng, Liwen Liu, YongGuo Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

Feature selection based on information theory plays an important role in classification algorithm due to its computational efficiency and independent from classification method. It is widely used in many application areas like data mining, bioinformatics and machine learning. But drawbacks of these methods are the neglect of the feature interaction and overestimation of features significance due to the limitations of goal functions criterion. To address this problem, we proposed a new feature goal function RJMIM. The method employed joint mutual information and information interaction, which alleviates the shortcomings of overestimation of the feature significance as demonstrated both theoretically and experimentally. The experiments conducted to verify the performance of the proposed method, it compared with four well-known feature selection methods use three publically available datasets from UCI. The average classification accuracy and C4.5 classifier is used to assess the effectiveness of RJMIM method.

关键词： Classification (of information)

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A multi-modal clustering method for traditonal chinese medicine clinical data via media convergence

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 390-400页

作者： Jingna Si Ziwei Tian Dongmei Li Lei Zhang Lei Yao Wenjuan Jiang Jia Liu Runshun Zhang Xiaoping Zhang State Key Laboratory of Tree Genetics and Breeding College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina Department of Comparative Plant and Fungal Biology Wellcome Trust Millennium BuildingArdinglyUK School of Information Science and Technology Beijing Forestry UniversityBeijingChina Engineering Research Center for Forestry‐oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijingChina National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical SciencesBeijingChina University of Wisconsin‐Milwaukee MilwaukeeWisconsinUSA Experimental Research Center China Academy of Chinese Medical SciencesBeijingChina Guang'anmen Hospital China Academy of Chinese Medical SciencesBeijingChina

Media convergence is a media change led by technological *** media convergence technology to the study of clustering in chinese medicine can significantly exploit the advantages of media *** consistent and complementary information among multiple modalities through media convergence can provide technical support for *** article presents an approach based on Media Convergence and Graph convolution Encoder Clustering(MCGEC)for traditonal chinese medicine(TCM)clinical *** feeds modal information and graph structure from media information into a multi-modal graph convolution encoder to obtain the media feature representation learnt from multiple *** captures latent information from various modalities by fusion and optimises the feature representations and network architecture with learnt clustering *** experiment is conducted on real-world multimodal TCM clinical data,including information like images and *** has improved clustering results compared to the generic single-modal clustering methods and the current more advanced multi-modal clustering *** applied to TCM clinical datasets can achieve better *** multimedia features into clustering algorithms offers significant benefits compared to single-modal clustering approaches that simply concatenate features from different *** provides practical technical support for multi-modal clustering in the TCM field incorporating multimedia features.

关键词： graph convolutional encoder media convergence multi-modal clustering traditional chinese medicine

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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