Patterns最新文献_第2页

Why dignity is a troubling concept for AI ethics. 为什么尊严是人工智能伦理的一个令人不安的概念。

IF 6.7

Patterns Pub Date : 2025-03-14 DOI: 10.1016/j.patter.2025.101207

Jon Rueda, Txetxu Ausín, Mark Coeckelbergh, Juan Ignacio Del Valle, Francisco Lara, Belén Liedo, Joan Llorca Albareda, Heidi Mertes, Robert Ranisch, Vera Lúcia Raposo, Bernd C Stahl, Murilo Vilaça, Íñigo de Miguel

引用次数: 0

Affirming our commitment to diversity, equity, and inclusion. 重申我们对多元化、公平和包容的承诺。

IF 6.7

Patterns Pub Date : 2025-03-06 eCollection Date: 2025-03-14 DOI: 10.1016/j.patter.2025.101204

Alejandra Alvarado, Wanying Wang, Andrew L Hufton

引用次数: 0

Ethical-Lens: Curbing malicious usages of open-source text-to-image models. 伦理镜头：遏制开源文本到图像模型的恶意使用。

IF 6.7

Patterns Pub Date : 2025-03-03 eCollection Date: 2025-03-14 DOI: 10.1016/j.patter.2025.101187

Yuzhu Cai, Sheng Yin, Yuxi Wei, Chenxin Xu, Weibo Mao, Felix Juefei-Xu, Siheng Chen, Yanfeng Wang

{"title":"Ethical-Lens: Curbing malicious usages of open-source text-to-image models.","authors":"Yuzhu Cai, Sheng Yin, Yuxi Wei, Chenxin Xu, Weibo Mao, Felix Juefei-Xu, Siheng Chen, Yanfeng Wang","doi":"10.1016/j.patter.2025.101187","DOIUrl":"https://doi.org/10.1016/j.patter.2025.101187","url":null,"abstract":"The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALL·E 3, has revolutionized content creation across diverse sectors. However, these advances bring forth critical ethical concerns, particularly with the misuse of open-source models to generate content that violates societal norms. Addressing this, we introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools without necessitating internal model revision. Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions by refining user commands and rectifying model outputs. Systematic evaluation metrics, combining GPT4-V, HEIM, and FairFace scores, assess alignment capability. Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models such as DALL <math><mrow><mo>·</mo></mrow> </math> E 3, while preserving the quality of generated images. This study indicates the potential of Ethical-Lens to promote the sustainable development of open-source text-to-image tools and their beneficial integration into society.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 3","pages":"101187"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781255","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

Insights into transportation CO₂ emissions with big data and artificial intelligence. 利用大数据和人工智能洞察交通二氧化碳排放。

IF 6.7

Patterns Pub Date : 2025-03-03 eCollection Date: 2025-04-11 DOI: 10.1016/j.patter.2025.101186

Zhenyu Luo, Tingkun He, Zhaofeng Lv, Junchao Zhao, Zhining Zhang, Yongyue Wang, Wen Yi, Shangshang Lu, Kebin He, Huan Liu

引用次数: 0

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs. 由基因平行对驱动的肿瘤免疫治疗合成反应的灵敏检测。

IF 6.7

Patterns Pub Date : 2025-02-25 eCollection Date: 2025-03-14 DOI: 10.1016/j.patter.2025.101184

Chuanpeng Dong, Feifei Zhang, Emily He, Ping Ren, Nipun Verma, Xinxin Zhu, Di Feng, James Cai, Hongyu Zhao, Sidi Chen

{"title":"Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs.","authors":"Chuanpeng Dong, Feifei Zhang, Emily He, Ping Ren, Nipun Verma, Xinxin Zhu, Di Feng, James Cai, Hongyu Zhao, Sidi Chen","doi":"10.1016/j.patter.2025.101184","DOIUrl":"10.1016/j.patter.2025.101184","url":null,"abstract":"Immunotherapies, including checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy, have revolutionized cancer treatment; however, many patients remain unresponsive to these treatments or relapse following treatment. CRISPR screenings have been used to identify novel single gene targets that can enhance immunotherapy effectiveness, but the identification of combinational targets remains a challenge. Here, we introduce a computational approach that uses sgRNA set enrichment analysis to identify cancer-intrinsic paralog pairs for enhancing immunotherapy using genome-wide screens. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features to predict paralog gene pairs that influence immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using CRISPR double knockout (DKO). These data and analyses collectively provide a sensitive approach to identifying previously undetected paralog gene pairs that can significantly affect cancer immunotherapy response, even when individual genes within the pair have limited effect.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 3","pages":"101184"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781266","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

Unraveling overoptimism and publication bias in ML-driven science. 揭示机器学习驱动科学中的过度乐观和发表偏见。

IF 6.7

Patterns Pub Date : 2025-02-25 eCollection Date: 2025-04-11 DOI: 10.1016/j.patter.2025.101185

Pouria Saidi, Gautam Dasarathy, Visar Berisha

{"title":"Unraveling overoptimism and publication bias in ML-driven science.","authors":"Pouria Saidi, Gautam Dasarathy, Visar Berisha","doi":"10.1016/j.patter.2025.101185","DOIUrl":"https://doi.org/10.1016/j.patter.2025.101185","url":null,"abstract":"Machine learning (ML) is increasingly used across many disciplines with impressive reported results. However, recent studies suggest that the published performances of ML models are often overoptimistic. Validity concerns are underscored by findings of an inverse relationship between sample size and reported accuracy in published ML models, contrasting with the theory of learning curves where accuracy should improve or remain stable with increasing sample size. This paper investigates factors contributing to overoptimism in ML-driven science, focusing on overfitting and publication bias. We introduce a stochastic model for observed accuracy, integrating parametric learning curves and the aforementioned biases. We construct an estimator that corrects for these biases in observed data. Theoretical and empirical results show that our framework can estimate the underlying learning curve, providing realistic performance assessments from published results. By applying the model to meta-analyses of classifications of neurological conditions, we estimate the inherent limits of ML-driven prediction in each domain.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 4","pages":"101185"},"PeriodicalIF":6.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12010447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051825","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

SplitFusion enables ultrasensitive gene fusion detection and reveals fusion variant-associated tumor heterogeneity. SplitFusion能够实现超灵敏的基因融合检测，并揭示融合变异相关的肿瘤异质性。

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. 欧盟人工智能法案的教训。

IF 6.7

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

Alejandra Alvarado

引用次数: 0

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals. GREEN：使用可学习小波和黎曼几何的轻量级架构，用于脑电图信号的生物标记物探测。

IF 6.7

Patterns Pub Date : 2025-02-13 eCollection Date: 2025-03-14 DOI: 10.1016/j.patter.2025.101182

Joseph Paillard, Jörg F Hipp, Denis A Engemann

{"title":"GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals.","authors":"Joseph Paillard, Jörg F Hipp, Denis A Engemann","doi":"10.1016/j.patter.2025.101182","DOIUrl":"10.1016/j.patter.2025.101182","url":null,"abstract":"Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 3","pages":"101182"},"PeriodicalIF":6.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781257","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

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