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19th International Workshop on data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Swaminathan, Arjhun Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning Department of Computer Science University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

ISBN: (纸本)9783031823480

With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to anonymize static datasets—allowing the creation of multiple generalizations for different anonymization needs from a single computation—the application to dynamic datasets remains unexplored. To address this, our work adapts existing methodologies to the dynamic setting. We develop an improved version of weighted persistence barcodes that track higher-dimensional holes in data, allowing us to edit persistence information on the fly. Additionally, we introduce filtration trimming, a novel technique designed to update persistence data quickly with minimal computing effort when data is added. Our work represents a significant advancement in healthcare data privacy, offering a refined approach to protecting highly sensitive and evolving patient data through dynamic k-anonymity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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CH-YOLO-Lite: a lightweight object detection model with context-aware progressive aggregation and hierarchical feature aggregation for aerial imagery

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Journal of Electronic Imaging 2025年第2期34卷

作者： Wang, Le Jiang, Wangyu Dharejo, Fayaz Ali Sun, Meng Timofte, Radu Mao, Guojun Fujian University of Technology School of Computer Science and Mathematics Fuzhou China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Technology Innovation Center of Factored Transaction Data in Tourist Attractions Ministry of Culture and Tourism Fuzhou China University of Wurzburg Computer Vision Lab CAIDAS IFI Wurzburg Germany University of Wurzburg Biocenter Department of Bioinformatics Wurzburg Germany

Object detection in aerial images poses significant challenges due to the high proportion of small objects, drastic scale variations, and resource constraints. We propose a lightweight object detection model, CH-YOLO-Lite, based on context-aware progressive aggregation and hierarchical feature augmentation. First, we designed a context-aware progressive aggregator (CPA) to integrate multi-scale contextual information through a progressive fusion strategy, enhancing the model's perception of its environment and improving small object detection in complex scenes. Second, we designed a hierarchical feature aggregation (HFA) module and an improved adaptive spatial feature fusion (iASFF) method to construct the feature augmentation neck, which significantly enhances the model's multi-scale detection capability in scenarios dominated by small objects. The HFA introduces more low-level detail features into the feature pyramid through cross-level branches, strengthening the model's feature representation for small objects. Meanwhile, iASFF adaptively learns the spatial fusion weights of feature maps at different scales, effectively solving the issue of scale inconsistency during feature fusion, further improving detection accuracy. Finally, to further simplify the model, channel pruning is used, which significantly reduces the parameters of the model while maintaining the detection performance. Experiments on the test set of the VisDrone dataset show that CH-YOLO-Lite significantly improves mAP0.5 by 7.2% and reduces parameters by 67% compared with the baseline model YOLOv7-Tiny. The mAP0.5 of CH-YOLO-Lite is also improved by 4.9% on NWPU VHR-10 datasets, which validates the generalization of our proposed algorithm. © 2025 SPIE and IS&T.

关键词： Aerial photography

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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年第10期70卷 1659-1672页

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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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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The Effect of Multi-Task Learning on the Prediction of Neoantigen–MHC Class II Binding

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and bioinformatics 2025年第2期22卷 570-580页

作者： Kazuhiro Ikkyu Itoshi Nikaido Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Life Science Innovation (Bioinformatics) University of Tsukuba Tsukuba Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Saitama Japan Department of Functional Genome Informatics Division of Biological Data Science Medical Research Laboratory (MRL) Institute of Integrated Research (IIR) Institute of Science Tokyo Bunkyo Japan

Neoepitopes are significant therapeutic cancer vaccine candidates, given that tumor neoepitopes induce an immune response to eliminate cancer cells. This immune activation depends on the binding affinity between the antigen peptide and the major histocompatibility complex (MHC). The epitope–MHC binding assay is a technologically difficult, time-consuming, and expensive technique. Therefore, prediction methods for these binding affinities have been developed using computational prediction approaches. However, these predictive models are trained on datasets biased toward viral peptides and some MHC alleles and are limited in their prediction of neoepitopes. In particular, because of the wide variety of MHC class II binding formats, the performance of MHC class II prediction must be improved. Here, we propose a novel deep learning model that consists of multi-task bidirectional long short-term memory (Bi-LSTM) models. Our multi-task model can predict neoepitope–MHC class II bindings from limited training data by sharing MHC class I and II training parameters. We confirm that multi-task learning significantly enhances the prediction performances of cancer antigens. Our model achieves an area under the receiver operating characteristic curve (AUC-ROC) of 82.2%, outperforming existing state-of-the-art single allele neoantigen prediction models while maintaining its strong generalization performance.

关键词： Peptides Predictive models Multitasking Antigens Cancer data models Training Computer architecture Training data Immune system

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Technologies Supporting Self-Reflection on Social Interactions: A Systematic Review 25

Technologies Supporting Self-Reflection on Social Interactio...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Hao, Chenxu Matej Hrkalovic, Tiffany Balliet, Daniel Hung, Hayley Dudzik, Bernd Delft University of Technology Delft Netherlands Jheronimus Academy of Data Science 's-Hertogenbosch Netherlands Tilburg University Tilburg Netherlands Vrije Universiteit Amsterdam Amsterdam Netherlands Intelligent Systems Delft University of Technology Delft Netherlands Intelligent Systems/Pattern Recognition and Bioinformatics Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713064

As intelligent technology and applications have become an integral part of nearly all aspects of people's daily lives, many intelligent systems have been designed to help people navigate the complex space of social interactions. One prominent strategy for such intelligent support is providing meaningful Ad Hoc Interventions (ADI), e.g., through timely notifications. An alternative is Technology-Supported Reflection (TSR), e.g., by offering information about activities in one's past for personal insights. In contrast to straight-up interventions, the aim of the latter strategy is not to directly augment human skills but instead support learning and personal growth over time. However, while TSR has seen widespread interest in applications in some areas, such as physical fitness and mental health, its use for improving human social interactions has not yet been systematically explored. Concretely, it is currently unclear 1) what forms of self-reflection systems intend to support, 2) how their different technological components (e.g., data collection, information integration) are involved in providing support, and 3) what common limitations and design challenges they face. In this article, we present the results of a systematic literature review focusing on these questions to provide a structured foundation for targeted research. Concretely, we identified and analysed a collection of 23 relevant papers, each describing a system deploying TSR to support humans with elements of social *** constructed a framework with a set of features to comprehensively describe and analyze the systems that support self-reflection, including their application domains, how they fit into the existing design framework, how they facilitate learning through reflection, how adaptive they are to individual users, and how they were evaluated. Finally, we propose a direction for designing systems that support individual's social interactions through self-reflection in an adaptive manne

关键词： Human computer interaction

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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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Graphical Transformation Models

arXiv

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arXiv 2025年

作者： Herp, Matthias Brachem, Johannes Altenbuchinger, Michael Kneib, Thomas Statistics University of Göttingen Germany Institute Data Science University of Göttingen Campus Germany Department of Medical Bioinformatics University Medical Center Göttingen Germany

Graphical Transformation Models (GTMs) are introduced as a novel approach to effectively model multivariate data with intricate marginals and complex dependency structures non-parametrically, while maintaining interpretability through the identification of varying conditional independencies. GTMs extend multivariate transformation models by replacing the Gaussian copula with a custom-designed multivariate transformation, offering two major advantages. Firstly, GTMs can capture more complex interdependencies using penalized splines, which also provide an efficient regularization scheme. Secondly, we demonstrate how to approximately regularize GTMs using a lasso penalty towards pairwise conditional independencies, akin to Gaussian graphical models. The model’s robustness and effectiveness are validated through simulations, showcasing its ability to accurately learn parametric vine copulas and identify conditional independencies. Additionally, the model is applied to a benchmark astrophysics dataset, where the GTM demonstrates favorable performance compared to non-parametric vine copulas in learning complex multivariate distributions. © 2025, CC BY.

关键词： Astrophysics

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Fine-tuning protein language models to understand the functional impact of missense variants

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Computational and Structural Biotechnology Journal 2025年 27卷 2199-2207页

作者： Ali Saadat Jacques Fellay School of Life Sciences Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland Precision Medicine Unit Biomedical Data Science Center Lausanne University Hospital and University of Lausanne Lausanne Switzerland

Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we trained a fully connected neural network classifier on frozen embeddings and compared its performance to fine-tuning in order to quantify the added value of task-specific adaptation. We then used the fine-tuned models to: 1) identify protein features enriched in either pathogenic or benign missense variants, and 2) compare the predicted feature profiles of proteins with reference and alternate alleles to understand how missense variants affect protein functionality. We show that our models can be used to reclassify variants of uncertain significance and provide mechanistic insights into the functional consequences of missense mutations.

关键词： Missense variant Mechanistic interpretation Protein language models Fine-tuning Token classification

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