Patterns最新文献_第2页

Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology. 双水平图学习揭示了乳腺多重数字病理中与预后相关的肿瘤微环境模式。

IF 6.7

Patterns Pub Date : 2025-02-11 eCollection Date: 2025-03-14 DOI: 10.1016/j.patter.2025.101178

Zhenzhen Wang, Cesar A Santa-Maria, Aleksander S Popel, Jeremias Sulam

{"title":"Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology.","authors":"Zhenzhen Wang, Cesar A Santa-Maria, Aleksander S Popel, Jeremias Sulam","doi":"10.1016/j.patter.2025.101178","DOIUrl":"10.1016/j.patter.2025.101178","url":null,"abstract":"The tumor microenvironment (TME) is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. Increasing efforts have been dedicated to characterizing it, including its analysis with modern deep learning. However, identifying generalizable biomarkers has been limited by the uninterpretable nature of their predictions. We introduce a data-driven yet interpretable approach for identifying cellular patterns in the TME associated with patient prognoses. Our method relies on constructing a bi-level graph model: a cellular graph, which models the TME, and a population graph, capturing inter-patient similarities given their respective cellular graphs. We demonstrate our approach in breast cancer, showing that the identified patterns provide a risk-stratification system with new complementary information to standard clinical subtypes, and these results are validated in two independent cohorts. Our methodology could be applied to other cancer types more generally, providing insights into the spatial cellular patterns associated with patient outcomes.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 3","pages":"101178"},"PeriodicalIF":6.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11962943/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention heads of large language models. 大型语言模型的注意头。

IF 6.7

Patterns Pub Date : 2025-02-06 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2025.101176

Zifan Zheng, Yezhaohui Wang, Yuxin Huang, Shichao Song, Mingchuan Yang, Bo Tang, Feiyu Xiong, Zhiyu Li

引用次数: 0

Model interpretability enhances domain generalization in the case of textual complexity modeling. 在文本复杂性建模的情况下，模型可解释性增强了领域泛化。

IF 6.7

Patterns Pub Date : 2025-02-06 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2025.101177

Frans van der Sluis, Egon L van den Broek

{"title":"Model interpretability enhances domain generalization in the case of textual complexity modeling.","authors":"Frans van der Sluis, Egon L van den Broek","doi":"10.1016/j.patter.2025.101177","DOIUrl":"10.1016/j.patter.2025.101177","url":null,"abstract":"Balancing prediction accuracy, model interpretability, and domain generalization (also known as [a.k.a.] out-of-distribution testing/evaluation) is a central challenge in machine learning. To assess this challenge, we took 120 interpretable and 166 opaque models from 77,640 tuned configurations, complemented with ChatGPT, 3 probabilistic language models, and Vec2Read. The models first performed text classification to derive principles of textual complexity (task 1) and then generalized these to predict readers' appraisals of processing difficulty (task 2). The results confirmed the known accuracy-interpretability trade-off on task 1. However, task 2's domain generalization showed that interpretable models outperform complex, opaque models. Multiplicative interactions further improved interpretable models' domain generalization incrementally. We advocate for the value of big data for training, complemented by (1) external theories to enhance interpretability and guide machine learning and (2) small, well-crafted out-of-distribution data to validate models-together ensuring domain generalization and robustness against data shifts.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 2","pages":"101177"},"PeriodicalIF":6.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ETHOS.REFLOW: An open-source workflow for reproducible renewable energy potential assessments. 风气。REFLOW：可再生能源潜力评估的开源工作流程。

IF 6.7

Patterns Pub Date : 2025-02-04 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2025.101172

Tristan Pelser, Jann Michael Weinand, Patrick Kuckertz, Detlef Stolten

{"title":"ETHOS.REFLOW: An open-source workflow for reproducible renewable energy potential assessments.","authors":"Tristan Pelser, Jann Michael Weinand, Patrick Kuckertz, Detlef Stolten","doi":"10.1016/j.patter.2025.101172","DOIUrl":"10.1016/j.patter.2025.101172","url":null,"abstract":"Accurate renewable energy resource assessments are necessary for energy system planning to meet climate goals, yet inconsistencies in methods and data can produce significant differences in results. This paper introduces ETHOS.REFLOW, a Python-based workflow manager that ensures transparency and reproducibility in energy potential assessments. The tool enables reproducible analyses with minimal effort by automating the entire workflow, from data acquisition to reporting. We demonstrate its functionality by estimating the technical offshore wind potential of the North Sea, for fixed-foundation and mixed-technology (including floating turbines) scenarios. Two methods for turbine siting (explicit placement vs. uniform power density) and wind datasets are compared. Results show a maximum installable capacity of 768-861 GW and an annual yield of 2,961-3,047 TWh, with capacity factors between 41% and 46% and significant temporal variability. ETHOS.REFLOW offers a robust framework for reproducible energy potential studies, enabling energy system modelers to build on existing work and fostering trust in findings.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 2","pages":"101172"},"PeriodicalIF":6.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-assisted facial analysis in healthcare: From disease detection to comprehensive management. ai辅助面部分析在医疗保健中的应用：从疾病检测到综合管理。

IF 6.7

Patterns Pub Date : 2025-02-04 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2025.101175

Chaoyu Lei, Kang Dang, Sifan Song, Zilong Wang, Sien Ping Chew, Ruitong Bian, Xichen Yang, Zhouyu Guan, Claudia Isabel Marques de Abreu Lopes, Mini Hang Wang, Richard Wai Chak Choy, Xiaoyan Hu, Kenneth Ka Hei Lai, Kelvin Kam Lung Chong, Chi Pui Pang, Xuefei Song, Jionglong Su, Xiaowei Ding, Huifang Zhou

{"title":"AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.","authors":"Chaoyu Lei, Kang Dang, Sifan Song, Zilong Wang, Sien Ping Chew, Ruitong Bian, Xichen Yang, Zhouyu Guan, Claudia Isabel Marques de Abreu Lopes, Mini Hang Wang, Richard Wai Chak Choy, Xiaoyan Hu, Kenneth Ka Hei Lai, Kelvin Kam Lung Chong, Chi Pui Pang, Xuefei Song, Jionglong Su, Xiaowei Ding, Huifang Zhou","doi":"10.1016/j.patter.2025.101175","DOIUrl":"10.1016/j.patter.2025.101175","url":null,"abstract":"Medical conditions and systemic diseases often manifest as distinct facial characteristics, making identification of these unique features crucial for disease screening. However, detecting diseases using facial photography remains challenging because of the wide variability in human facial features and disease conditions. The integration of artificial intelligence (AI) into facial analysis represents a promising frontier offering a user-friendly, non-invasive, and cost-effective screening approach. This review explores the potential of AI-assisted facial analysis for identifying subtle facial phenotypes indicative of health disorders. First, we outline the technological framework essential for effective implementation in healthcare settings. Subsequently, we focus on the role of AI-assisted facial analysis in disease screening. We further expand our examination to include applications in health monitoring, support of treatment decision-making, and disease follow-up, thereby contributing to comprehensive disease management. Despite its promise, the adoption of this technology faces several challenges, including privacy concerns, model accuracy, issues with model interpretability, biases in AI algorithms, and adherence to regulatory standards. Addressing these challenges is crucial to ensure fair and ethical use. By overcoming these hurdles, AI-assisted facial analysis can empower healthcare providers, improve patient care outcomes, and enhance global health.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 2","pages":"101175"},"PeriodicalIF":6.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum: A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection. 一种用于估计SARS-CoV-2感染后医院特异性急性后医疗保健需求的潜在迁移学习方法

IF 6.7

Patterns Pub Date : 2025-01-23 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2025.101179

Qiong Wu, Nathan M Pajor, Yiwen Lu, Charles J Wolock, Jiayi Tong, Vitaly Lorman, Kevin B Johnson, Jason H Moore, Christopher B Forrest, David A Asch, Yong Chen

引用次数: 0

Erratum: Decorrelative network architecture for robust electrocardiogram classification. 校正：鲁棒心电图分类的去相关网络架构。

IF 6.7

Patterns Pub Date : 2025-01-22 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2025.101180

Christopher Wiedeman, Ge Wang

引用次数: 0

Erratum: Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving. 勘误：数据驱动的电动汽车能耗评估，用于将标准测试推广到实际驾驶。

IF 6.7

Patterns Pub Date : 2025-01-22 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2025.101173

Xinmei Yuan, Jiangbiao He, Yutong Li, Yu Liu, Yifan Ma, Bo Bao, Leqi Gu, Lili Li, Hui Zhang, Yucheng Jin, Long Sun

引用次数: 0

Unraveling the complexity of rat object vision requires a full convolutional network and beyond. 解开老鼠物体视觉的复杂性需要一个完整的卷积网络，甚至更多。

IF 6.7

Patterns Pub Date : 2025-01-17 eCollection Date: 2025-02-14 DOI: 10.1016/j.patter.2024.101149

Paolo Muratore, Alireza Alemi, Davide Zoccolan

引用次数: 0

A survey of multilingual large language models. 多语种大型语言模型综述。

IF 6.7

Patterns Pub Date : 2025-01-10 DOI: 10.1016/j.patter.2024.101118

Libo Qin, Qiguang Chen, Yuhang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, Philip S Yu

引用次数: 0