IF 6.7

Patterns Pub Date : 2025-03-14 DOI: 10.1016/j.patter.2025.101208

Melania Muñoz-García, Amber Hartman Scholz

引用次数: 0

Strategies to include prior knowledge in omics analysis with deep neural networks. 在深度神经网络组学分析中包含先验知识的策略。

IF 6.7

Patterns Pub Date : 2025-03-14 DOI: 10.1016/j.patter.2025.101203

Kisan Thapa, Meric Kinali, Shichao Pei, Augustin Luna, Özgün Babur

引用次数: 0

Data shadows: When data become tangible, material, and fragile. 数据阴影：当数据变得有形、物质化和脆弱时。

IF 6.7

Patterns Pub Date : 2025-03-14 DOI: 10.1016/j.patter.2025.101206

Paul Trauttmansdorff, Kim M Hajek

引用次数: 0

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

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

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

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