Patterns最新文献

SplitFusion enables ultrasensitive gene fusion detection and reveals fusion variant-associated tumor heterogeneity.

IF 6.7

Patterns Pub Date : 2025-02-14 DOI: 10.1016/j.patter.2025.101174

Weiwei Bian, Baifeng Zhang, Zhengbo Song, Binyamin A Knisbacher, Yee Man Chan, Chloe Bao, Chunwei Xu, Wenxian Wang, Athena Hoi Yee Chu, Chenyu Lu, Hongxian Wang, Siyu Bao, Zhenyu Gong, Hoi Yee Keung, Zi-Ying Maggie Chow, Yiping Zhang, Wah Cheuk, Gad Getz, Valentina Nardi, Mengsu Yang, William Chi Shing Cho, Jian Wang, Juxiang Chen, Zongli Zheng

{"title":"SplitFusion enables ultrasensitive gene fusion detection and reveals fusion variant-associated tumor heterogeneity.","authors":"Weiwei Bian, Baifeng Zhang, Zhengbo Song, Binyamin A Knisbacher, Yee Man Chan, Chloe Bao, Chunwei Xu, Wenxian Wang, Athena Hoi Yee Chu, Chenyu Lu, Hongxian Wang, Siyu Bao, Zhenyu Gong, Hoi Yee Keung, Zi-Ying Maggie Chow, Yiping Zhang, Wah Cheuk, Gad Getz, Valentina Nardi, Mengsu Yang, William Chi Shing Cho, Jian Wang, Juxiang Chen, Zongli Zheng","doi":"10.1016/j.patter.2025.101174","DOIUrl":"10.1016/j.patter.2025.101174","url":null,"abstract":"Gene fusions are common cancer drivers and therapeutic targets, but clinical-grade open-source bioinformatic tools are lacking. Here, we introduce a fusion detection method named SplitFusion, which is fast by leveraging Burrows-Wheeler Aligner-maximal exact match (BWA-MEM) split alignments, can detect cryptic splice-site fusions (e.g., EML4::ALK v3b and ARv7), call fusions involving highly repetitive gene partners (e.g., CIC::DUX4), and infer frame-ness and exon-boundary alignments for functional prediction and minimizing false positives. Using 1,848 datasets of various sizes, SplitFusion demonstrated superior sensitivity and specificity compared to three other tools. In 1,076 formalin-fixed paraffin-embedded lung cancer samples, SplitFusion identified novel fusions and revealed that EML4::ALK variant 3 was associated with multiple fusion variants coexisting in the same tumor. Additionally, SplitFusion can call targeted splicing variants. Using data from 515 The Cancer Genome Atlas (TCGA) samples, SplitFusion showed the highest sensitivity and uncovered two cases of SLC34A2::ROS1 that were missed in previous studies. These capabilities make SplitFusion highly suitable for clinical applications and the study of fusion-defined tumor heterogeneity.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 2","pages":"101174"},"PeriodicalIF":6.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558204","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

Lessons from the EU AI Act.

IF 6.7

Patterns Pub Date : 2025-02-14 DOI: 10.1016/j.patter.2025.101183

Alejandra Alvarado

引用次数: 0

Attention heads of large language models.

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.

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.

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.

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.

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.

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.

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.

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