IEEE Computer Graphics and Applications最新文献

Circuit Mining in Transcriptomics Data. 转录组学数据中的电路挖掘。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-07-31 DOI: 10.1109/MCG.2025.3594562

Tobias Peherstorfer, Sophia Ulonska, Bianca Burger, Simone Lucato, Bader Al-Hamdan, Marvin Kleinlehner, Till F M Andlauer, Katja Buhler

引用次数: 0

Enhancing Visual Analysis in Person Re-Identification With Vision-Language Models. 用视觉语言模型增强人再识别中的视觉分析。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-07-28 DOI: 10.1109/MCG.2025.3593227

Wang Xia, Tianci Wang, Jiawei Li, Guodao Sun, Haidong Gao, Xu Tan, Ronghua Liang

引用次数: 0

Challenges and Opportunities for the Visualization of Protein Energy Landscapes. 蛋白质能量景观可视化的挑战与机遇。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-07-28 DOI: 10.1109/MCG.2025.3592983

Jaume Ros, Alessio Arleo, Rafael Giordano Viegas, Vitor B P Leite, Fernando V Paulovich

引用次数: 0

A Cosmic View of Life on Earth: Hierarchical Visualization of Biological Data Using Astronomical Software. 地球生命的宇宙观：使用天文软件的生物数据分层可视化。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-07-22 DOI: 10.1109/MCG.2025.3591713

Wandrille Duchemin, Takanori Fujiwara, Hollister W Herhold, Elias Elmquist, David S Thaler, William Harcourt-Smith, Emma Broman, Alexander Bock, Brian P Abbott, Jacqueline K Faherty

{"title":"A Cosmic View of Life on Earth: Hierarchical Visualization of Biological Data Using Astronomical Software.","authors":"Wandrille Duchemin, Takanori Fujiwara, Hollister W Herhold, Elias Elmquist, David S Thaler, William Harcourt-Smith, Emma Broman, Alexander Bock, Brian P Abbott, Jacqueline K Faherty","doi":"10.1109/MCG.2025.3591713","DOIUrl":"https://doi.org/10.1109/MCG.2025.3591713","url":null,"abstract":"A goal of data visualization is to advance the understanding of multi-parameter, large-scale datasets. In astrophysics, scientists map celestial objects to understand the hierarchical structure of the universe. In biology, genetic sequences and biological characteristics uncover evolutionary relationships and patterns (e.g., variation within species and ecological associations). Our highly interdisciplinary project entitled \"A Cosmic View of Life on Earth\" adapts an immersive astrophysics visualization platform called OpenSpace to contextualize diverse biological data. Dimensionality reduction techniques harmonize biological information to create spatial representations in which data are interactively explored on flat screens and planetarium domes. Visualizations are enriched with geographic metadata, three-dimensional scans of specimens, and species-specific sonifications (e.g., bird songs). The \"Cosmic View\" project eases the dissemination of stories related to biological domains (e.g., insects, birds, mammals, human migrations) and facilitates scientific discovery.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Age-based Study into Interactive Narrative Visualization Engagement. 基于年龄的交互式叙事可视化参与研究。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-07-22 DOI: 10.1109/MCG.2025.3591817

Nina Errey, Yi Chen, Yu Dong, Quang Vinh Nguyen, Xiaoru Yuan, Tuck Wah Leong, Christy Jie Liang

引用次数: 0

Do Language Model Agents Align with Humans in Rating Visualizations? An Empirical Study. 语言模型代理在评价可视化方面与人类一致吗？实证研究。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-07-09 DOI: 10.1109/MCG.2025.3586461

Zekai Shao, Yi Shan, Yixuan He, Yuxuan Yao, Junhong Wang, Xiaolong Zhang, Yu Zhang, Siming Chen

{"title":"Do Language Model Agents Align with Humans in Rating Visualizations? An Empirical Study.","authors":"Zekai Shao, Yi Shan, Yixuan He, Yuxuan Yao, Junhong Wang, Xiaolong Zhang, Yu Zhang, Siming Chen","doi":"10.1109/MCG.2025.3586461","DOIUrl":"https://doi.org/10.1109/MCG.2025.3586461","url":null,"abstract":"Large language models (LLMs) show potential in understanding visualizations and may capture design knowledge. However, their ability to predict human feedback remains unclear. To explore this, we conduct three studies evaluating the alignment between LLM-based agents and human ratings in visualization tasks. The first study replicates a human-subject study, showing promising agent performance in human-like reasoning and rating, and informing further experiments. The second study simulates six prior studies using agents and finds alignment correlates with experts' pre-experiment confidence. The third study tests enhancement techniques like input preprocessing and knowledge injection, revealing limitations in robustness and potential bias. These findings suggest that LLM-based agents can simulate human ratings when guided by high-confidence hypotheses from expert evaluators. We also demonstrate the usage scenario in rapid prototype evaluation and discuss future directions. We note that simulation may only serve as complements and cannot replace user studies.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The State of Single-Cell Atlas Data Visualization in the Biological Literature. 生物学文献中单细胞图谱数据可视化的现状。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-06-27 DOI: 10.1109/MCG.2025.3583979

Mark S Keller, Eric Morth, Thomas C Smits, Simon Warchol, Grace Guo, Qianwen Wang, Robert Krueger, Hanspeter Pfister, Nils Gehlenborg

引用次数: 0

ProHap Explorer: Visualizing Haplotypes in Proteogenomic Datasets. ProHap Explorer：可视化蛋白质基因组数据集中的单倍型。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-06-20 DOI: 10.1109/MCG.2025.3581736

Jakub Vasicek, Dafni Skiadopoulou, Ksenia G Kuznetsova, Lukas Kall, Marc Vaudel, Stefan Bruckner

{"title":"ProHap Explorer: Visualizing Haplotypes in Proteogenomic Datasets.","authors":"Jakub Vasicek, Dafni Skiadopoulou, Ksenia G Kuznetsova, Lukas Kall, Marc Vaudel, Stefan Bruckner","doi":"10.1109/MCG.2025.3581736","DOIUrl":"10.1109/MCG.2025.3581736","url":null,"abstract":"In mass spectrometry-based proteomics, experts usually project data onto a single set of reference sequences, overlooking the influence of common haplotypes (combinations of genetic variants inherited together from a parent). We recently introduced ProHap, a tool for generating customized protein haplotype databases. Here, we present ProHap Explorer, a visualization interface designed to investigate the influence of common haplotypes on the human proteome. It enables users to explore haplotypes, their effects on protein sequences, and the identification of non-canonical peptides in public mass spectrometry datasets. The design builds on well-established representations in biological sequence analysis, ensuring familiarity for domain experts while integrating novel interactive elements tailored to proteogenomic data exploration. User interviews with proteomics experts confirmed the tool's utility, highlighting its ability to reveal whether haplotypes affect proteins of interest. By facilitating the intuitive exploration of proteogenomic variation, ProHap Explorer supports research in personalized medicine and the development of targeted therapies.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AuraGenome: An LLM-Powered Framework for On-the-Fly Reusable and Scalable Circular Genome Visualizations. AuraGenome：一个llm驱动的框架，用于动态可重用和可扩展的圆形基因组可视化。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-06-20 DOI: 10.1109/MCG.2025.3581560

Chi Zhang, Yu Dong, Yang Wang, Yuetong Han, Guihua Shan, Bixia Tang

{"title":"AuraGenome: An LLM-Powered Framework for On-the-Fly Reusable and Scalable Circular Genome Visualizations.","authors":"Chi Zhang, Yu Dong, Yang Wang, Yuetong Han, Guihua Shan, Bixia Tang","doi":"10.1109/MCG.2025.3581560","DOIUrl":"10.1109/MCG.2025.3581560","url":null,"abstract":"Circular genome visualizations are essential for exploring structural variants and gene regulation. However, existing tools often require complex scripting and manual configuration, making the process time-consuming, error-prone, and difficult to learn. To address these challenges, we introduce AuraGenome, an LLM-powered framework for rapid, reusable, and scalable generation of multi-layered circular genome visualizations. AuraGenome combines a semantic-driven multi-agent workflow with an interactive visual analytics system. The workflow employs seven specialized LLM-driven agents, each assigned distinct roles such as intent recognition, layout planning, and code generation, to transform raw genomic data into tailored visualizations. The system supports multiple coordinated views tailored for genomic data, offering ring, radial, and chord-based layouts to represent multi-layered circular genome visualizations. In addition to enabling interactions and configuration reuse, the system supports real-time refinement and high-quality report export. We validate its effectiveness through two case studies and a comprehensive user study. AuraGenome is available at: https://github.com/Darius18/AuraGenome.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FEWSim: A Visual Analytic Framework for Exploring the Nexus of Food-Energy-Water Simulations. FEWSim：探索食物-能量-水模拟关系的可视化分析框架。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-06-19 DOI: 10.1109/MCG.2025.3581004

Fan Lei, David A Sampson, Jiayi Hong, Yuxin Ma, Giuseppe Mascaro, Dave White, Rimjhim Agarwal, Ross Maciejewski

{"title":"FEWSim: A Visual Analytic Framework for Exploring the Nexus of Food-Energy-Water Simulations.","authors":"Fan Lei, David A Sampson, Jiayi Hong, Yuxin Ma, Giuseppe Mascaro, Dave White, Rimjhim Agarwal, Ross Maciejewski","doi":"10.1109/MCG.2025.3581004","DOIUrl":"https://doi.org/10.1109/MCG.2025.3581004","url":null,"abstract":"The interdependencies of food, energy, and water (FEW) systems create a nexus opportunity to explore the strengths and vulnerabilities of individual and cross-sector interactions within FEW systems. However, the variables quantifying nexus interactions are hard to observe, which hinders the cross-sector analysis. To overcome such challenges, we present FEWSim, a visual analytics framework designed to support domain experts in exploring and interpreting simulation results from a coupled FEW model. FEWSim employs a three-layer asynchronous architecture: the model layer integrates food, energy, and water models to simulate the FEW nexus; the middleware layer manages scenario configuration and execution; and the visualization layer provides interactive visual exploration of simulated time-series results across FEW sectors. The visualization layer further facilitates the exploration across multiple scenarios and evaluates scenario differences in performance using sustainability indices of the FEW nexus. We demonstrate the utility of FEWSim through a case study for the Phoenix Active Management Area (AMA) in Arizona.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0