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DARG:An integrated knowledge base for analyzing addictive drug-related genes

Genes & Diseases 2025年第2期12卷 140-142页

作者： Xu Wang Bei Yun Zihan Zhang Xiaoxi Wang Yifan Wu Yubo Hu Shiyi Fang Junjie Lv Lina Chen Wan Li College of Bioinformatics Science and Technology Harbin Medical UniversityHarbinHeilongjiang 150081China

Drug addiction is a complex brain disease closely related to the expression and methylation of many *** genes can influence addiction,and some key differential genes may have a significant impact on the occurrence and development of ***,data on addictive drugs is widely *** our knowledge,there are very few databases on genes related to addictive drugs,and existing addiction related databases are not available.

关键词： drugs dic addiction

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Enrichment Analysis and Deep Learning in Biomedical Ontology:Applications and Advancements

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Chinese Medical sciences Journal 2025年第1期40卷 45-56,I0006页

作者： Hong-Yu Fu Yang-Yang Liu Mei-Yi Zhang Hai-Xiu Yang College of Bioinformatics Science and Technology Harbin Medical UniversityHarbin 150081China

Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research *** ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical *** this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology *** analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular *** learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and *** the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.

关键词： biomedical ontology enrichment analysis deep learning ontology hierarchy ontology annotation

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A review of recent artificial intelligence for traditional medicine

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Journal of Traditional and Complementary Medicine 2025年第3期15卷 215-228页

作者： Hou, Chengbin Gao, Yanzhuo Lin, Xinyu Wu, Jinchao Li, Ning Lv, Hairong Chu, William Cheng-Chung School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fujian 350109 China Maynooth International Engineering College Fuzhou University Fuzhou 350108 China MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China

Traditional Medicine (TM) has played a crucial role in global healthcare due to its long history and holistic approach. Artificial Intelligence (AI) has emerged as a revolutionary technology, offering exceptional capabilities in areas such as data mining, pattern recognition, and decision-making. The integration of Artificial Intelligence for Traditional Medicine (AITM) presents a promising frontier in advancing medicine and healthcare. In this review, we explore AITM from two perspectives: recent AI techniques and TM applications. Specifically, we investigate how Machine Learning, Deep Learning, and Large Language Models are applied to TM, covering applications such as diagnosis (before, during, after) and research (drug research, structured knowledge, data analysis). By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. We anticipate this review can bridge the gap between AI and TM communities. And the goal is to foster collaboration and innovation between both communities, enabling them to exploit the state-of-the-art AI techniques to advance TM diagnosis and research, ultimately contributing to the enhancement of human health. © 2025 Center for Food and Biomolecules, National Taiwan University

关键词： AI for health AI for medicine Artificial intelligence Deep learning Large language models Machine learning Traditional medicine

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SpaGRA:Graph augmentation facilitates domain identification for spatially resolved transcriptomics

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Journal of Genetics and Genomics 2025年第1期52卷 93-104页

作者： Xue Sun Wei Zhang Wenrui Li Na Yu Daoliang Zhang Qi Zou Qiongye Dong Xianglin Zhang Zhiping Liu Zhiyuan Yuan Rui Gao Center of Intelligent Medicine School of Control Science and EngineeringShandong UniversityJinanShandong 250061China MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of AutomationTsinghua UniversityBeijing 100084China Institute of Precision Medicine Peking University Shenzhen HospitalShenzhenGuangdong 518036China Department of Clinical Laboratory The Second HospitalCheeloo College of MedicineShandong UniversityJinanShandong 250033China Institute of Science and Technology for Brain-Inspired Intelligence Center for Medical Research and InnovationShanghai Pudong HospitalFudan University Pudong Medical CenterFudan UniversityShanghai 200433China

Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various ***-based geometric deep learning has gained widespread adoption for spatial domain identification ***,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain *** tackle this challenge,we propose a novel method,SpaGRA(https://***/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph *** uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive ***,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random *** results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different *** SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD ***,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

关键词： Spatial domain identification Spatially resolved transcriptomics Multi-head graph attention networks Graph augmentation Geometric contrastive learning

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ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response

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Journal of Translational Medicine 2025年第1期23卷 1-15页

作者： Li, Xiangmei Pan, Bingyue Zhao, Xilong Su, Yinchun Lai, Jiyin Li, Siyuan He, Yalan Wu, Jiashuo Han, Junwei College of Bioinformatics Science and Technology Harbin Medical University Harbin 150081 China Department of Neurobiology Harbin Medical University Harbin 150081 China

Background: Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. Methods: Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. Results: Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. Conclusions: Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology. © The Author(s) 2025.

关键词： Driver module Immune checkpoint inhibitors Mutually exclusive mutation Prognostic biomarkers Therapeutic targets

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Alternative splicing of immune-related genes identifies breast cancer subtypes with differential immune cell infiltration

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Genes & Diseases 2025年第2期12卷 121-124页

作者： Zhangxiang Zhao Yuquan Wang Zixin Jin Huiming Han Bo Chen Mingyue Liu Kaidong Liu Shuping Zhuang Haihai Liang Yunyan Gu The Sino-Russian Medical Research Center of Jinan University The Institute of Chronic Disease of Jinan UniversityThe First Affiliated Hospital of Jinan UniversityGuangzhouGuangdong 510632China Department of Pharmacology(State-Province Key Laboratories of Biomedicine-Pharmaceutics of China Key Laboratory of Cardiovascular ResearchMinistry of Education)College of PharmacyHarbin Medical UniversityHarbinHeilongjiang 150081China Department of Systems Biology College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbinHeilongjiang 150081China

Breast cancer,recognized as a foremost cause of mortality among women,is characterized by a complex immune *** splicing(AS)is a crucial process leading to diverse transcript *** have shown As's role in T cell and B cell *** instance,Shalek et al performed single-cell RNA sequencing and showed that different AS patterns existed depending on the maturity and differentiation of dendritic cells,such as Cxcl10.

关键词： cancer mortality immune

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Microbial resources and interactions across three-dimensional space for a freshwater ecosystem

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science of the Total Environment 2025年 980卷 179522页

作者： Chu, Dongliang Zhang, Haohong Wang, Zhi Ning, Kang Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imaging Center of AI Biology Department of Bioinformatics and Systems Biology College of Life Science and Technology Huazhong University of Science and Technology Wuhan China Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan430077 China

Freshwater ecosystems are important natural resources but face serious threats. Nevertheless, they host diverse microorganisms crucial for biosynthetic potential and global biochemical cycles. To fully understand the enrichment and interaction of species and functional resources in freshwater ecosystems, it is essential to profile the microbial resources in the whole three-dimensional space. We profiled 131 metagenomic samples to construct the Honghu Microbial Catalog, comprising 2617 metagenome-assembled genomes, 1718 candidate species, over 60 million non-redundant gene clusters, and 7396 biosynthetic gene clusters. We emphasized surface water may be the primary source of microbial species and ARGs for Honghu Lake. We also found the impact of surface water on groundwater had an "influence sphere". Furthermore, we have identified groundwater as a potential refuge for microbial resources, enriched with CPR bacteria and ARGs. These findings are crucial for the understanding, management, and protection of freshwater ecosystems. © 2025 The Authors

关键词： Metagenomic Three-dimensional space Genome and gene catalog Microbial refuge

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Single-cell technologies: current and near future

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science China(Life sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Correction:Inference and prioritization of tissue-specific regulons in Arabidopsis and Oryza

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aBIOTECH 2025年第1期6卷 139-139页

作者： Honggang Dai Yaxin Fan Yichao Mei Ling-Ling Chen Junxiang Gao Hubei Key Laboratory of Agricultural Bioinformatics College of InformaticsHuazhong Agricultural UniversityWuhan 430070China State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources College of Life Science and TechnologyGuangxi UniversityNanning 530004China

Correction:aBIOTECH(2024)5:309-324 https://***/10.1007/s42994-024-00176-2 The article Inference and prioritization of tissue-specific regulons in Arabidopsis and Oryza,written by Honggang Dai,Yaxin Fan,Yichao Mei,Ling-Ling Chen,Junxiang Gao,was originally published Online First without Open *** publication in volume 5,issue 3,pages 309–324 the authors decided to opt for Open Choice and to make the article an Open Access publication.

关键词： Arabidopsis prioritization inference Oryza open access open access publication open choice tissue specific regulons

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An automatic annotation tool and reference database for T cell subtypes and states at single-cell resolution

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science Bulletin 2025年

作者： Shen, Wen-Kang Zhang, Chu-Yu Gu, Yi-Min Luo, Tao Chen, Si-Yi Yue, Tao Xie, Gui-Yan Liao, Yu Yuan, Yong Lei, Qian Guo, An-Yuan Department of Thoracic Surgery West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China Hubei Bioinformatics & Molecular Imaging Key Laboratory College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Life Sciences University of Chinese Academy of Sciences Beijing101408 China Department of Rheumatology & Immunology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430074 China

T cells have various subtypes and states with different functions. However, a reference list and automated annotation tool for T cell subtypes and states are lacking, which is critical for analyzing and comparing T cells under various conditions. We constructed the largest human T cell reference, containing 1,348,268 T cells from 35 conditions and 16 tissues. We classified T cells into 33 subtypes and further stratified them into 68 categories according to subtype and state. Based on this reference, we developed a tool named STCAT to automatically annotate T cells from scRNA-seq data by hierarchical models and marker correction. The accuracy of STCAT was 28% higher than that of existing tools validated on six independent datasets, including cancer and healthy samples. Using STCAT, we consistently discovered that CD4+ Th17 cells were enriched in late-stage lung cancer patients in multiple datasets, whereas MAIT cells were prevalent in milder-stage COVID-19 patients. We also confirmed a decrease in Treg cytotoxicity in post-treatment ovarian cancer. Systematic landscape analyses of CD4+ and CD8+ T cell references revealed that CD4+ Treg cells were enriched in tumor samples and that CD8+ naive-related cells were abundant in healthy individuals. Finally, we deposited all the T cell references and annotations into a TCellAtlas (https://***/TCellAtlas) database, which allows users to browse T cell expression profiles and analyze customized scRNA-seq data by STCAT. In conclusion, comprehensive human T cell subtypes and states reference, automated annotation tool, and database will greatly facilitate research on T cell immunity and tumor immunology. © 2025 science China Press

关键词： T-cells

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