Patterns最新文献_第6页

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems. 勘误：人工智能作为数字健康和农业食品系统的协调范例。

IF 10.8

Patterns Pub Date : 2026-05-13 eCollection Date: 2026-06-12 DOI: 10.1016/j.patter.2026.101584

Anand K Gavai, Miranda P M Meuwissen

引用次数: 0

Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics. 基于遗传算法的脑动力学能量景观分析区域选择自动化。

IF 10.8

Patterns Pub Date : 2026-05-11 eCollection Date: 2026-07-10 DOI: 10.1016/j.patter.2026.101560

Koichiro Mori, Tomoyuki Hiroyasu, Satoru Hiwa

引用次数: 0

Encouraging productive struggle in the age of generative AI. 在生成式人工智能时代鼓励富有成效的斗争。

IF 7.4

Patterns Pub Date : 2026-05-08 DOI: 10.1016/j.patter.2026.101540

Andrei Biswas, Rajagopal Venkatesaramani

引用次数: 0

Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care. 以边缘化为中心：人工智能驱动的罕见病护理卫生公平推进战略。

IF 10.8

Patterns Pub Date : 2026-05-08 DOI: 10.1016/j.patter.2026.101535

Chaoyu Lei, Ying Zuo, Claudia Abreu Lopes, Jaimee Stuart, Benjamin Xu, T Y Alvin Liu, Thomas Ploug, Zilong Wang, Kang Dang, Kai Jin, Haoxuan Yu, Heaven Yeshaneh Tatere, Fanyi Kong, Ning Zhang, Lufa Zhang, Huifang Zhou

{"title":"Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.","authors":"Chaoyu Lei, Ying Zuo, Claudia Abreu Lopes, Jaimee Stuart, Benjamin Xu, T Y Alvin Liu, Thomas Ploug, Zilong Wang, Kang Dang, Kai Jin, Haoxuan Yu, Heaven Yeshaneh Tatere, Fanyi Kong, Ning Zhang, Lufa Zhang, Huifang Zhou","doi":"10.1016/j.patter.2026.101535","DOIUrl":"10.1016/j.patter.2026.101535","url":null,"abstract":"Rare diseases (RDs) affect 6%-8% of the global population but remain critically underserved. People living with an RD face misdiagnosis, limited treatment options, and inequitable access to specialized care. While artificial intelligence (AI) offers transformative potential in RD care, significant challenges remain. This perspective identifies five key dimensions to equitable AI application in RD care: data availability, algorithmic fairness, patient privacy, resource prioritization, and medical ethics. To address these barriers, strategies include enhancing data diversity through internationally harmonized repositories, leveraging synthetic data, and employing fairness-aware algorithms. Privacy-preserving methods safeguard sensitive genetic data while enabling collaborative research. Transparent resource-allocation frameworks and interdisciplinary governance ensure equitable distribution of AI-driven benefits, particularly in low- and middle-income countries. Ethical considerations, including patient-centered consent and dynamic risk assessments, are foundational to sustainable AI integration. By addressing these multidisciplinary challenges, AI can advance health equity, transforming RD care from fragmented and inequitable to inclusive and innovative. This paradigm shift aligns technological progress with the ethical imperative to ensure no patient is left behind in the promise of precision medicine.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 5","pages":"101535"},"PeriodicalIF":10.8,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147934161","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

Multimodal integrated knowledge transfer to large language models through preference optimization with biomedical applications. 通过生物医学应用偏好优化，多模态集成知识转移到大型语言模型。

IF 10.8

Patterns Pub Date : 2026-05-04 eCollection Date: 2026-08-14 DOI: 10.1016/j.patter.2026.101544

Zhanliang Wang, Da Wu, Quan M Nguyen, Zhuoran Xu, Kai Wang

{"title":"Multimodal integrated knowledge transfer to large language models through preference optimization with biomedical applications.","authors":"Zhanliang Wang, Da Wu, Quan M Nguyen, Zhuoran Xu, Kai Wang","doi":"10.1016/j.patter.2026.101544","DOIUrl":"10.1016/j.patter.2026.101544","url":null,"abstract":"The scarcity of high-quality multimodal biomedical data limits the effective fine-tuning of large language models (LLMs) for specialized tasks. We introduce MINT (multimodal integrated knowledge transfer), a framework that aligns unimodal decoder-only models with domain-specific patterns from multimodal biomedical data through preference optimization and that is primarily implemented using the odds ratio preference optimization (ORPO) framework. MINT leverages upstream multimodal machine-learning models to transfer domain expertise to downstream text-only or image-only LLMs, as demonstrated in two applications: (1) rare genetic disease prediction from texts and (2) tissue-type classification using cell nucleus images. In both cases, MINT-based models outperform those enhanced with alternative approaches such as supervised fine-tuning (SFT) and retrieval-augmented generation (RAG), even surpassing much larger foundation models in some scenarios. Our study highlights how MINT effectively grafts the classification strengths of encoder-only models into large decoder-only models, thereby enhancing reasoning abilities and reducing hallucination in biomedical applications.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 8","pages":"101544"},"PeriodicalIF":10.8,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799095","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

Deep learning enabled prediction of nuclear lamina-associated chromatin. 深度学习能够预测核层相关染色质。

IF 10.8

Patterns Pub Date : 2026-04-27 eCollection Date: 2026-08-14 DOI: 10.1016/j.patter.2026.101545

Priyojit Das, Jeannie T Lee

{"title":"Deep learning enabled prediction of nuclear lamina-associated chromatin.","authors":"Priyojit Das, Jeannie T Lee","doi":"10.1016/j.patter.2026.101545","DOIUrl":"10.1016/j.patter.2026.101545","url":null,"abstract":"Tethering of chromatin regions to the nuclear lamina contributes to genome organization and gene regulation. Though several molecular and epigenomic determinants of lamina association have been studied, the influence of genomic sequences has received less attention. Here, we present lamina-associated domain finder (LADDER), a multimodal deep learning model to predict lamina association based on DNA sequence, gene density, and long interspersed nuclear element (LINE)1 and short interspersed nuclear element (SINE) densities. The predictions made by LADDER are cell-type independent. Using LADDER's predictive ability and performing in silico genome-wide random deletions, we established an intrinsic genome lamina susceptibility landscape, where lamina perturbations are enriched near boundary-centered architectural transition zones. Pathogenic structural variants and deletions of CTCF-binding sites alter lamina tethering predominantly when they localize near these structurally sensitive regions. Applying LADDER across species, we examined allele-specific differential lamina association of mouse escapee genes. We further demonstrated the effectiveness of integrating LADDER-predicted nonspecific lamina-tethering data to improve the 3D chromatin models across genomic scales.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 8","pages":"101545"},"PeriodicalIF":10.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799130","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

Bridging annotated microscopy imaging data and analysis method development for scientific discovery. 连接注释显微镜成像数据和科学发现分析方法的发展。

IF 10.8

Patterns Pub Date : 2026-04-23 eCollection Date: 2026-06-12 DOI: 10.1016/j.patter.2026.101542

Kevin A Yamauchi, Virginie Uhlmann

{"title":"Bridging annotated microscopy imaging data and analysis method development for scientific discovery.","authors":"Kevin A Yamauchi, Virginie Uhlmann","doi":"10.1016/j.patter.2026.101542","DOIUrl":"10.1016/j.patter.2026.101542","url":null,"abstract":"While modern imaging technologies offer unprecedented opportunities to observe life across scales, distilling an understanding of the underlying biological processes from these complex, high-dimensional data remains challenging. Computational analysis methods have been lagging behind our ability to produce data, as their development often requires expertise across multiple domains, including life and computer sciences. Annotated image datasets play a key role in fostering the development and improvement of microscopy image analysis methods, as they offer a realistic basis to build upon and invaluable ground truth to evaluate and optimize performance. Drawing inspiration from adjacent fields to microscopy imaging, we discuss in this perspective how sharing annotated datasets has driven progress in computational analysis. We emphasize the critical role that open data standards and infrastructure play in realizing the full scientific potential of annotated image datasets and close by highlighting opportunities for members across the scientific community to cultivate a dynamic ecosystem of data, infrastructure, and analysis methods to elevate research quality and accelerate innovation.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 6","pages":"101542"},"PeriodicalIF":10.8,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13280720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148296628","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

MANNERS: A strategy for representation learning in multivariate datasets with high proportions of missing data. 一种在具有高缺失数据比例的多变量数据集中进行表示学习的策略。

IF 10.8

Patterns Pub Date : 2026-04-23 eCollection Date: 2026-07-10 DOI: 10.1016/j.patter.2026.101543

Louis Bellmann, Maximilian Nielsen, Philipp Breitfeld

{"title":"MANNERS: A strategy for representation learning in multivariate datasets with high proportions of missing data.","authors":"Louis Bellmann, Maximilian Nielsen, Philipp Breitfeld","doi":"10.1016/j.patter.2026.101543","DOIUrl":"10.1016/j.patter.2026.101543","url":null,"abstract":"Missing data present a major challenge for deep learning, and various imputation techniques exist. However, imputation quality generally decreases as missing data rates increase. In time-series data from electronic health records, missing rates for individual parameters up to 99% can be observed, posing a problem for mere imputation. In addition, underlying patterns between missing and observed data can contain valuable information that can be learned by deep learning models. In this work, we propose a strategy called missing adjusted normalization and nullity encoding representation strategy (MANNERS), which can be applied to training pipelines for representation learning. MANNERS encodes missingness, masks loss evaluation at missing data points, and applies rebalancing such that the signal from variables with high missing rates is not lost. We evaluated MANNERS on reconstruction and downstream classification, regression, and synthetic data generation tasks. We showed performance improvements in the presence of very high missing rates compared with state-of-the-art imputation-only techniques.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 7","pages":"101543"},"PeriodicalIF":10.8,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13366519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449636","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

Pitfalls and risks of generative AI in machine learning. 生成式人工智能在机器学习中的陷阱和风险。

IF 10.8

Patterns Pub Date : 2026-04-22 eCollection Date: 2026-05-08 DOI: 10.1016/j.patter.2026.101534

Michael A Lones

引用次数: 0

A multi-modal foundation model for brain disease diagnosis and medical imaging. 脑疾病诊断和医学影像的多模态基础模型。

IF 10.8

Patterns Pub Date : 2026-04-14 eCollection Date: 2026-06-12 DOI: 10.1016/j.patter.2026.101538

Guoxun Zhang, Zebin Gao, Caohui Duan, Jiaxin Liu, Yuerong Lizhu, Yaou Liu, Qian Chen, Ling Wang, Kailun Fei, Tianyun Wang, YuJia Chen, Yanchen Guo, Feng Xu, Yuchen Guo, Xin Lou, Qionghai Dai

{"title":"A multi-modal foundation model for brain disease diagnosis and medical imaging.","authors":"Guoxun Zhang, Zebin Gao, Caohui Duan, Jiaxin Liu, Yuerong Lizhu, Yaou Liu, Qian Chen, Ling Wang, Kailun Fei, Tianyun Wang, YuJia Chen, Yanchen Guo, Feng Xu, Yuchen Guo, Xin Lou, Qionghai Dai","doi":"10.1016/j.patter.2026.101538","DOIUrl":"10.1016/j.patter.2026.101538","url":null,"abstract":"The precise and comprehensive diagnosis of complex brain disorders relies on non-invasive computed tomography (CT) and magnetic resonance imaging (MRI) in conjunction with multi-modal clinical information. Here, we present Brainfound, a multi-modal foundation model for brain medical imaging that integrates image-text contrastive learning with a diffusion-based generative framework. The model was pre-trained on more than 3 million brain CT slices and 7 million brain MRI slices paired with clinical reports. In multi-center evaluations, Brainfound demonstrates state-of-the-art performance across seven tasks, including brain disease diagnosis, lesion segmentation, MRI enhancement, cross-modality translation, automatic report generation, zero-shot disease classification, and human-AI dialogue. It substantially outperforms leading models in automated report generation and clinical question answering for brain imaging, and its performance approaches that of expert physicians. These findings highlight the potential of Brainfound for accelerating diagnosis, support treatment decisions, and advance human-in-the-loop brain health care.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 6","pages":"101538"},"PeriodicalIF":10.8,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13280722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148296647","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