Patterns最新文献_第2页

Erratum: Navigating COP16's digital sequence information outcomes: What researchers need to do in practice. 勘误：导航COP16的数字序列信息结果：研究人员在实践中需要做什么。

IF 10.8

Patterns Pub Date : 2026-09-02 eCollection Date: 2026-09-11 DOI: 10.1016/j.patter.2026.101661

Melania Muñoz-García, Amber Hartman Scholz

引用次数: 0

The cost of reproducibility in artificial intelligence. 人工智能的可重复性成本。

IF 10.8

Patterns Pub Date : 2026-08-27 eCollection Date: 2026-10-09 DOI: 10.1016/j.patter.2026.101640

Thijs Snelleman, Holger Hoos, Odd Erik Gundersen

{"title":"The cost of reproducibility in artificial intelligence.","authors":"Thijs Snelleman, Holger Hoos, Odd Erik Gundersen","doi":"10.1016/j.patter.2026.101640","DOIUrl":"10.1016/j.patter.2026.101640","url":null,"abstract":"Insufficiently documented publications can make independent reproduction an unnecessarily laborious task. We define the cost of reproducibility as the resources required to reproduce the findings of a study and we propose five dimensions of cost, using objective characteristics such as code and data. We review 918 publications between 2022 and 2024 at top AI venues to evaluate the variation in reproducibility costs, with the hypothesis that publications at venues with stricter standards are less costly. Papers published at machine learning conferences are less costly to reproduce than publication from other venues, 12.91% less costly compared with journals, and 16.52% less costly compared with general AI conferences. Award-winning papers, however, are not less costly to reproduce than regular papers, and 6.72% of implementation repository links were broken. These findings suggest that reproducibility costs can be significantly reduced through compliance-based mechanisms-provided they receive community support. We recommend that venues require the inclusion of reproducibility checklists in their publications, permit appendices for comprehensive documentation, and adopt low reproducibility cost as a key criterion for awards.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 10","pages":"101640"},"PeriodicalIF":10.8,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217041","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

Q-SID: Detecting collusion groups from exam question scores with error quantification. Q-SID：用误差量化方法从考试题目分数中检测合谋组。

IF 10.8

Patterns Pub Date : 2026-08-25 eCollection Date: 2026-10-09 DOI: 10.1016/j.patter.2026.101642

Guanao Yan, Jingyi Jessica Li, Mark D Biggin

{"title":"Q-SID: Detecting collusion groups from exam question scores with error quantification.","authors":"Guanao Yan, Jingyi Jessica Li, Mark D Biggin","doi":"10.1016/j.patter.2026.101642","DOIUrl":"10.1016/j.patter.2026.101642","url":null,"abstract":"Collusion in online exams undermines the integrity of exam results. Existing detection methods identify suspicious student pairs and are often limited to multiple-choice formats. We introduce Question-Score Identity Detection (Q-SID), a statistical algorithm that efficiently detects likely collusion groups and quantifies uncertainty using only graded numeric question scores. This enables Q-SID to support a broad range of multiple-choice and non-multiple-choice exams. Q-SID reports two false-positive rates (FPRs) for each collusion group: (1) an empirical FPR, whose null data are from strictly proctored exam datasets, and (2) a synthetic FPR, whose null data are simulated from a copula-based model. Across 34 unproctored exam datasets, including two benchmark datasets with verified positives and negatives from textual analysis, Q-SID performs robustly across exam formats, class sizes, numbers of questions, and levels of complexity. It is more accurate and substantially faster than widely used pairwise statistics and group-level baselines, including answer similarity at group level (ASIg) and clique detection.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 10","pages":"101642"},"PeriodicalIF":10.8,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674315/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217001","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: MedicalAgentsBench for complex medical reasoning: Comparing internalized reasoning models versus externalized agent-based frameworks. 用于复杂医学推理的MedicalAgentsBench：比较内部化推理模型与外部化基于代理的框架。

IF 10.8

Patterns Pub Date : 2026-08-25 eCollection Date: 2026-09-11 DOI: 10.1016/j.patter.2026.101658

Yanjun Shao, Xiangru Tang, Jiwoong Sohn, Jiapeng Chen, Yuxuan Liao, Jiayi Zhang, Jinyu Xiang, Fang Wu, Yilun Zhao, Chenglin Wu, Wenqi Shi, Arman Cohan, Mark Gerstein

引用次数: 0

Retrieval-augmented reasoning reshapes collective reliability in text-based radiology question answering. 检索增强推理重塑了基于文本的放射学问题回答的集体可靠性。

IF 10.8

Patterns Pub Date : 2026-08-19 eCollection Date: 2026-10-09 DOI: 10.1016/j.patter.2026.101639

Mina Farajiamiri, Jeta Sopa, Saba Afza, Lisa Adams, Felix Barajas Ordonez, Tri-Thien Nguyen, Mahshad Lotfinia, Sebastian Wind, Keno Bressem, Sven Nebelung, Daniel Truhn, Soroosh Tayebi Arasteh

{"title":"Retrieval-augmented reasoning reshapes collective reliability in text-based radiology question answering.","authors":"Mina Farajiamiri, Jeta Sopa, Saba Afza, Lisa Adams, Felix Barajas Ordonez, Tri-Thien Nguyen, Mahshad Lotfinia, Sebastian Wind, Keno Bressem, Sven Nebelung, Daniel Truhn, Soroosh Tayebi Arasteh","doi":"10.1016/j.patter.2026.101639","DOIUrl":"10.1016/j.patter.2026.101639","url":null,"abstract":"Retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in clinical question answering, but their reliability under model variability remains unclear. We evaluated 34 LLMs on 169 expert-curated, text-only, multiple-choice radiology questions, comparing zero-shot inference with a fixed shared-evidence condition in which all models received identical structured evidence reports before answer selection. Primary endpoints were inter-model decision dispersion and panel-based robustness of correctness. Shared evidence reduced decision dispersion (median entropy 0.48→0.13; p = 5.6 × 10-9) and increased robustness of correctness (mean 0.74→0.81; p = 5.6 × 10-9). Majority consensus increased, and consensus strength remained strongly correlated with robustness. However, high agreement did not guarantee correctness, and rare high-consensus failures persisted. These findings show that shared evidence can improve reproducibility under model variability, but accuracy and agreement alone are insufficient to characterize reliability.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 10","pages":"101639"},"PeriodicalIF":10.8,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217004","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

Disentangling batch effects and biological signals across conditions in single-cell transcriptome. 解开单细胞转录组中不同条件下的批效应和生物信号。

IF 10.8

Patterns Pub Date : 2026-08-19 eCollection Date: 2026-10-09 DOI: 10.1016/j.patter.2026.101638

Shunta Sakaguchi, Masato Tsutsumi, Kentaro Nishi, Honda Naoki

{"title":"Disentangling batch effects and biological signals across conditions in single-cell transcriptome.","authors":"Shunta Sakaguchi, Masato Tsutsumi, Kentaro Nishi, Honda Naoki","doi":"10.1016/j.patter.2026.101638","DOIUrl":"10.1016/j.patter.2026.101638","url":null,"abstract":"Batch effects, which arise due to technical variations across different experimental factors, pose a significant challenge for single-cell transcriptomic analysis. Although various batch correction methods have been developed to mitigate these effects, they often indiscriminately mix data from different batches, leading to the removal of biologically meaningful signals. This limitation hinders comparative analyses across multiple conditions, an essential aspect of scientific research. Recent approaches attempt to address this issue by mapping data to separate spaces, but they prevent direct comparisons between conditions. Here, we propose Kanade, a batch correction method based on a variational autoencoder. Kanade explicitly disentangles batch effects from biological signals by specializing latent variables for different types of information. Using both simulated and real datasets, we demonstrate that Kanade selectively corrects batch effects while preserving essential biological features, enabling more accurate comparative analyses in single-cell transcriptomics.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 10","pages":"101638"},"PeriodicalIF":10.8,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216942","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

RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI. 鲁棒py：一个高效的下一代多通用库，具有模型选择、平均、重采样和可解释的人工智能。

IF 10.8

Patterns Pub Date : 2026-08-14 DOI: 10.1016/j.patter.2026.101609

Daniel Valdenegro, Jiani Yan, Duiyi Dai, Charles Rahal

{"title":"RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI.","authors":"Daniel Valdenegro, Jiani Yan, Duiyi Dai, Charles Rahal","doi":"10.1016/j.patter.2026.101609","DOIUrl":"10.1016/j.patter.2026.101609","url":null,"abstract":"Scientific inference is undermined by the vast but rarely explored \"multiverse\" of defensible modeling choices, which can generate results as variable as the phenomena under study. We introduce RobustiPy, an open-source Python library that systematizes multiverse analysis and model uncertainty quantification at scale. RobustiPy unifies bootstrap-based inference, combinatorial specification search, model selection and averaging, joint inference routines, and explainable AI methods within a modular, reproducible framework. Beyond exhaustive specification curves, it supports rigorous out-of-sample validation and provides feature-level explanations for the full-specification predictive model. We demonstrate utility across five simulations and ten high-profile replications spanning economics, sociology, psychology, and medicine, including a re-analysis of widely cited findings with documented discrepancies. Benchmarking on ∼672 million simulated regressions shows that RobustiPy delivers state-of-the-art computational efficiency while expanding transparency. By standardizing and accelerating methods for robustness, RobustiPy transforms how researchers interrogate sensitivity across the analytical multiverse, offering a foundation for reproducible and interpretable computational science.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 8","pages":"101609"},"PeriodicalIF":10.8,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799129","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

From data to value in multimodal learning. 从数据到多模态学习的价值。

IF 10.8

Patterns Pub Date : 2026-08-14 DOI: 10.1016/j.patter.2026.101615

Chunlei Tang, Xing Jia, Yun Xiong

引用次数: 0

EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology. EmmaEmb：分子生物学中用于分析嵌入空间的定量框架。

IF 10.8

Patterns Pub Date : 2026-08-12 eCollection Date: 2026-09-11 DOI: 10.1016/j.patter.2026.101635

Pia Francesca Rissom, Vít Škrhák, Paulo Yanez Sarmiento, Jordan F Safer, Connor W Coley, Bernhard Y Renard, Henrike O Heyne, Sumaiya Iqbal

{"title":"EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.","authors":"Pia Francesca Rissom, Vít Škrhák, Paulo Yanez Sarmiento, Jordan F Safer, Connor W Coley, Bernhard Y Renard, Henrike O Heyne, Sumaiya Iqbal","doi":"10.1016/j.patter.2026.101635","DOIUrl":"10.1016/j.patter.2026.101635","url":null,"abstract":"Embeddings, numerical vectors learned by deep-learning models, are increasingly used to represent complex molecular biology data and support predictive tasks and generative design. There is a growing need for systematic approaches to interpret and explain the information encoded in high-dimensional embedding spaces. Here, we introduce EmmaEmb, a quantitative, model-agnostic framework for geometric correction, direct analysis, and comparison of embedding spaces. Our framework encompasses local and global analysis methods to quantify data distribution within an embedding space and enable comparisons of representations across spaces in relation to known biological features. Through experiments with seven embedding models across six molecular biology tasks, we demonstrate that our methods reveal insights from embedding spaces that align with downstream predictive tasks, uncover misclassification patterns, and contextualize differences in biological information captured by ProtT5, AlphaFold2, and ESM C. We provide an open-source Python library implementing all analysis methods and a guided diagnostic workflow.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 9","pages":"101635"},"PeriodicalIF":10.8,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978180","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

Hybrid artificial intelligence and quantum annealing as an optimization layer in drug discovery. 混合人工智能和量子退火作为药物发现的优化层。

IF 10.8

Patterns Pub Date : 2026-08-05 eCollection Date: 2026-08-14 DOI: 10.1016/j.patter.2026.101634

Chia-Ho Ou, Jun-Cheng Liao, Chung-Yao Huang, Oscar K Lee

{"title":"Hybrid artificial intelligence and quantum annealing as an optimization layer in drug discovery.","authors":"Chia-Ho Ou, Jun-Cheng Liao, Chung-Yao Huang, Oscar K Lee","doi":"10.1016/j.patter.2026.101634","DOIUrl":"10.1016/j.patter.2026.101634","url":null,"abstract":"Artificial intelligence (AI) has greatly expanded the generative capacity of drug discovery, yet the ability to translate large candidate pools into structured, resource-aware decisions remains limited. This review addresses this emerging optimization bottleneck by examining quantum annealing as a potential decision-optimization layer within hybrid computational workflows. We focus on discrete, constraint-dominated tasks-such as compound subset selection, combinatorial design, and multi-objective prioritization-that can be formulated as quadratic unconstrained binary optimization (QUBO) problems. Rather than positioning quantum annealing as a predictive tool, we analyze its role as a complementary optimization interface integrated with AI-generated scores. We further discuss practical implementation considerations, including embedding overhead, noise, scalability, and benchmarking challenges. By emphasizing workflow-level design and rigorous evaluation, this review provides a pragmatic framework for assessing annealing-based optimization in drug discovery and related data-intensive scientific domains.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 8","pages":"101634"},"PeriodicalIF":10.8,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799117","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