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TCMNP: A data processing and visualization database and R package for traditional chinese medicine network pharmacology

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Pharmacological Research - Modern chinese medicine 2025年 14卷

作者： Ying, Min Feng, Jing Liu, Mei Wang, Lijuan Liu, Jinkun Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer Hospital Chongqing 400030 China Chongqing Key Laboratory of Traditional Chinese Medicine to Prevent and Treat Autoimmune Diseases Chongqing Hospital of Traditional Chinese Medicine Chongqing 400021 China Department of Pharmacy Chongqing Hospital of Traditional Chinese Medicine Chongqing 400021 China Department of Pathology Chongqing Hospital of Traditional Chinese Medicine Chongqing 400021 China First Affiliated Hospital of Chongqing University of Chinese Medicine Chongqing University of Chinese Medicine Chongqing 400021 China

Background: Network pharmacology has become crucial for elucidating the mechanisms underlying traditional chinese medicine (TCM). However, challenges persist in processing and visualizing complex data related to TCM network pharmacology (TCMNP). To address this, we developed ''TCMNP'', a specialized database and R package within the R environment, aimed at enhancing data processing and visualization capabilities for TCM research. Methods: ''TCMNP'' integrates data from prominent databases, including DrugBank, TCMSP, ETCM, DisGeNET, and OMIM, to establish a comprehensive relational framework connecting TCM prescriptions, medicinal materials, bioactive ingredients, therapeutic targets, and associated diseases. Leveraging packages such as ''dplyr'', ''clusterProfiler'', ''ggplot2'', and ''circlize'', we created a suite of functions that facilitate streamlined data analysis and advanced visualizations within R. Results: ''TCMNP'' supports comprehensive analysis and visualization workflows for TCM compounds, individual herbs, active ingredients, molecular targets, and biological pathways. The database comprises 571 TCM herbs, 17,118 unique ingredients, 10,013 diseases, and 15,956 targets, enabling automated processes from compound component screening to target and pathway enrichment analysis. Additional features include protein-protein interaction mapping and transcription factor-gene target analysis, complemented by a user-friendly interface for generating TCM network pharmacology visualizations. ''TCMNP'' is accessible at https://***/tcmnp, and the package available at https://***/tcmlab/TCMNP. Conclusion: ''TCMNP'' database and package provide user-friendly visualization tools for analyzing the components of TCM formulas and their potential therapeutic effects on diseases. We anticipate that ''TCMNP'' will help to explore the mechanism of TCM to treat diseases. © 2024

关键词： Network pharmacology R package TCMNP Traditional chinese medicine Traditional chinese medicine network pharmacology

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Surface-enhanced Raman scattering on nanomaterials for cancer and pathogens diagnosis

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Nano Research 2025年第2期18卷 650-674页

作者： Jingjing Wu Baohong Zhang Lei Xu Yanyu Zhao Tao Chen Jie Lin Anran Li School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology of the People’s Republic of ChinaBeihang UniversityBeijing 100191China School of Optics and Photonics Beijing Institute of TechnologyBeijing 100089China Shanxi Lu'an Taihang Lubricant Technology Co. Ltd.Changzhi 046204China Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China

Surface-enhanced Raman scattering(SERS)is a non-invasive spectroscopic technique that provides specific chemical fingerprint information for biomarkers in cancer and pathogen ***,the SERS strategies are limited by the non-specific interactions between substrates and co-existing substances in biological matrices and the challenges of obtaining molecular fingerprint information from the complex vibrational *** recent years,the rapid development of novel substrates with high SERS activity has opened up new opportunities for their applications in cancer and pathogen *** aim of this review is to present the recent progress and perspectives of novel SERS-based substrates for cancer and pathogen diagnostic ***,we will introduce recently developed SERS-active nanomaterials and discuss the influencing factors of the SERS ***,the advantages of SERS in the diagnosis of cancer and pathogens will be ***,we will review the latest breakthroughs in cancer and pathogen detection research with SERS technology,as well as the new opportunities for SERS applications brought about by artificial intelligence(Al)*** addition,the novel microfluidic-SERS platforms for cancer and pathogens diagnosis will also be ***,we will summarize the challenges and future perspectives of SERS technology in the field of early cancer diagnosis and rapid pathogen *** is highly expected that this review could benefit a comprehensive understanding of the research status of the SERS-active nanomaterials and arouse the research enthusiasm for them,leading to accelerated clinical translation of SERS technology in cancer and pathogen diagnosis.

关键词： surface-enhanced Raman scattering(SERS) SERS-active nanomaterials cancer and pathogen diagnosis artificial intelligence(Al)technology

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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“Weibing” in traditional chinese medicine—biological basis and mathematical representation of disease-susceptible state

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Acta Pharmaceutica Sinica B 2025年第5期15卷 2363-2371页

作者： Sun, Wan-Yang Wang, Rong Ouyang, Shu-Hua Liang, Wan-Li Duan, Junwei Gong, Wen-Yong Hu, Lian-Ting Chen, Xiu-Juan Li, Yi-Fang Kurihara, Hiroshi Yao, Xin-Sheng Gao, Hao He, Rong-Rong Institute of Traditional Chinese Medicine and Natural Products College of Pharmacy/Guangdong Engineering Research Center of Traditional Chinese Medicine & Disease Susceptibility/Guangdong Engineering Research Center of Traditional Chinese Medicine & Health Products/International Cooperative Laboratory of TCM Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE)/Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research/State Key Laboratory of Bioactive Molecules and Druggability Assessment Jinan University Guangzhou 510632 China State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macau 999078 China College of Information Science and Technology Jinan University Guangzhou 510632 China Faculty of Data Science City University of Macau Macau 999078 China Medical Big Data Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou 510632 China The Data Center Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital) Tongji Medical College Huazhong University of Science and Technology Wuhan 430016 China

“Weibing” is a fundamental concept in traditional chinese medicine (TCM), representing a transitional state characterized by diminished self-regulatory abilities without overt physiological or social dysfunction. This perspective delves into the biological foundations and quantifiable markers of Weibing, aiming to establish a research framework for early disease intervention. Here, we propose the “Health Quadrant Classification” system, which divides the state of human body into health, sub-health, disease-susceptible state, and disease. We suggest the disease-susceptible stage emerges as a pivotal point for TCM interventions. To understand the intrinsic dynamics of this state, we propose laboratory and clinical studies utilizing time-series experiments and stress-induced disease susceptibility models. At the molecular level, bio-omics technologies and bioinformatics approaches are highlighted for uncovering intricate changes during disease progression. Furthermore, we discuss the application of mathematical models and artificial intelligence in developing early warning systems to anticipate and avert the transition from health to disease. This approach resonates with TCM's preventive philosophy, emphasizing proactive health maintenance and disease prevention. Ultimately, our perspective underscores the significance of integrating modern scientific methodologies with TCM principles to propel Weibing research and early intervention strategies forward. © 2025 The Authors

关键词： Disease-susceptible state Mathematical modeling Preventive medicine Traditional chinese medicine Weibing

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Nitrate ameliorates myelin loss and cognitive impairment in Alzheimer's disease through upregulation of neuronal sialin and subsequent inhibition of TPPP phosphorylation

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Science Bulletin 2025年第8期70卷 1224-1229页

作者： Chen, Xinyue Hu, Geng Chang, Lirong Li, Xiaoyu Tang, Yi Wu, Yan Zhang, Ran Zhang, Chunmei Wang, Jinsong Wang, Xue Wang, Songlin Department of Biochemistry and Molecular Biology School of Basic Medical Sciences Beijing Laboratory of Oral Health Capital Medical University Beijing100069 China Key Laboratory of Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing100191 China Beijing Key Laboratory of Neural Regeneration and Repair Department of Anatomy School of Basic Medical Sciences Beijing Institute of Brain Disorders Capital Medical University Beijing100069 China Innovation Center for Neurological Disorders Department of Neurology Xuanwu Hospital Capital Medical University Beijing100053 China Beijing Key Laboratory of Traditional Chinese Veterinary Medicine Department of Veterinary Medicine Animal Science and Technology College Beijing University of Agriculture Beijing102206 China Department of Oral Pathology Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China National Human Brain Bank for Development and Function Institute of Basic Medical Sciences Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical College Beijing10005 China Laboratory of Homeostatic Medicine School of Medicine Southern University of Science and Technology Shenzhen518055 China Research Units of Tooth Development and Regeneration Chinese Academy of Medical Sciences Beijing100700 China

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Offline Reinforcement Learning via Conservative Smoothing and Dynamics Controlling

Offline Reinforcement Learning via Conservative Smoothing an...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haihong Guo Fengxin Li Jiao Li Hongyan Liu School of Information Renmin University of China China Chinese Academy of Medical Sciences / Peking Union Medical College Institute of Medical Information / Medical Library China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Offline Reinforcement Learning (RL) optimizes policy using pre-collected data instead of direct environment interaction, offering a safe and cost-effective solution for sequential decision-making in the real world. However, it faces challenges such as distribution shift issues and vulnerability under perturbations. Researchers have developed various conservative methods to improve the robustness of offline RL. Nevertheless, model-based methods can result in transition distribution shift issues, while model-free value-based uncertainty penalty methods may not be sufficiently robust. To address these problems, we propose a new method called Robust Offline RL via Conservative Smoothing and Dynamics Controlling (RCSD). To achieve reliable value estimation of out-of-distribution (OOD) actions, RCSD uses both model-free uncertainty penalty and model-based simulation methods. It introduces a new one-step simulation method with conservative dynamics controlling to avoid value overestimation caused by transition distribution shifts. Moreover, RCSD considers both current and next states when generating OOD states to ensure cautious value estimation and efficient data utilization. RCSD uses conservative Q-smoothing and policy smoothing to strengthen the policy against sudden changes under perturbations. Experiments on D4RL benchmark demonstrate that RCSD can achieve state-of-the-art performance compared to baselines in either benchmark or adversarial attack tests.

关键词： Uncertainty Smoothing methods Perturbation methods Estimation Reinforcement learning Benchmark testing Signal processing Robustness Speech processing Faces

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haihong Guo Fengxin Li Jiao Li Hongyan Liu School of Information Renmin University of China China Institute of Medical Information / Medical Library Chinese Academy of Medical Sciences / Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Training Performance evaluation Hands Q-learning Neural networks Memory management Signal processing Benchmark testing Splines (mathematics) Speech processing

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Testing High-Dimensional Means for Sparse Signals

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Communications in Mathematics and Statistics 2025年 1-31页

作者： Zhu, Jiayan Yu, Xiaochen Pan, Dongdong Li, Zhengbang School of Information Engineering Hubei University of Chinese Medicine Wuhan China School of Mathematics and Statistics Central China Normal University Wuhan China Yunnan Key Laboratory of Statistical Modeling and Data Analysis School of Mathematics and Statistics Yunnan University Kunming China

In this paper, we investigate the high-dimensional mean vector testing problem with fewer observations than the dimension. We revisit a kind of test which belongs to the supremum-type statistic and propose a novel test by the combination of the maximum and minimum values among all component tests. The asymptotic null distributions of two tests are obtained and the asymptotical powers against sparse alternatives are also investigated under weak conditions. Simulation results show that these two tests can control empirical sizes well. Our novel test can gain desirable powers. A real data example about Electro-Encephalo Gram measurements also demonstrates that our proposed test has desirable numerical performances. © School of Mathematical Sciences, University of Science and Technology of China and Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Asymptotic theories for extremes Asymptotic theories for extremes Behrens-Fisher problem Behrens-Fisher problem High-dimensional central limit theorems High-dimensional central limit theorems High-dimensional data High-dimensional data

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NuHTC: A hybrid task cascade for nuclei instance segmentation and classification

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Medical Image Analysis 2025年 103卷 103595页

作者： Li, Bao Liu, Zhenyu Zhang, Song Liu, Xiangyu Sun, Caixia Liu, Jiangang Qiu, Bensheng Tian, Jie Center for Biomedical Imaging University of Science and Technology of China Hefei Anhui230026 China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing100191 China Key Laboratory of Big Data-Based Precision Medicine Beihang University Ministry of Industry and Information Technology Beijing100191 China

Nuclei instance segmentation and classification of hematoxylin and eosin (H&E) stained digital pathology images are essential for further downstream cancer diagnosis and prognosis tasks. Previous works mainly focused on bottom-up methods using a single-level feature map for segmenting nuclei instances, while multilevel feature maps seemed to be more suitable for nuclei instances with various sizes and types. In this paper, we develop an effective top-down nuclei instance segmentation and classification framework (NuHTC) based on a hybrid task cascade (HTC). The NuHTC has two new components: a watershed proposal network (WSPN) and a hybrid feature extractor (HFE). The WSPN can provide additional proposals for the region proposal network which leads the model to predict bounding boxes more precisely. The HFE at the region of interest (RoI) alignment stage can better utilize both the high-level global and the low-level semantic features. It can guide NuHTC to learn nuclei instance features with less intraclass variance. We conduct extensive experiments using our method in four public multiclass nuclei instance segmentation datasets. The quantitative results of NuHTC demonstrate its superiority in both instance segmentation and classification compared to other state-of-the-art methods. © 2025 The Authors

关键词： Semantic Segmentation

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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