Patterns最新文献

Human-centric AI: An interview with Edith Luhanga. 以人为中心的AI: Edith Luhanga访谈

IF 7.4

Patterns Pub Date : 2025-09-12 DOI: 10.1016/j.patter.2025.101369

Edith Luhanga

引用次数: 0

Facing the possibility of consciousness in human brain organoids. 面对人脑类器官中意识的可能性。

IF 7.4

Patterns Pub Date : 2025-09-12 DOI: 10.1016/j.patter.2025.101365

Christopher Wood, Hao Wang, Wei-Jun Yang, Yongmei Xi

{"title":"Facing the possibility of consciousness in human brain organoids.","authors":"Christopher Wood, Hao Wang, Wei-Jun Yang, Yongmei Xi","doi":"10.1016/j.patter.2025.101365","DOIUrl":"10.1016/j.patter.2025.101365","url":null,"abstract":"Human brain organoids (HBOs) have emerged as transformative models for neurodevelopment and disease, yet ethical concerns persist regarding their potential to develop consciousness. Since 2020, a growing cohort of neuroscientists and philosophers has dismissed these concerns as unscientific, citing limited structural complexity, absence of bodily integration and environmental interaction, and a prevailing neuroscientific consensus against the feasibility of any, or any near-future, emergence of HBO consciousness, thus challenging any suggested revisions of ethical guidelines and safeguards. We argue that this dismissal is premature. Drawing on neuroscientific benchmarks, comparisons to the developing human brain, contemporary theories of consciousness, and principles of natural developmental progression, we question the basis for selectively excluding consciousness from among HBOs' expanding functional repertoire. We caution against enshrining such skepticism into dogma or using it to defer ethical engagement. Instead, we advocate for proactive, ongoing assessment of the moral implications of advancing HBO capabilities.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 9","pages":"101365"},"PeriodicalIF":7.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214054","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

RDMkit: A research data management toolkit for life sciences. RDMkit：生命科学研究数据管理工具包。

IF 7.4

Patterns Pub Date : 2025-08-22 eCollection Date: 2025-09-12 DOI: 10.1016/j.patter.2025.101345

Pinar Alper, Flora D'Anna, Bert Droesbeke, Munazah Andrabi, Rafael Andrade Buono, Federico Bianchini, Korbinian Bösl, Ishwar Chandramouliswaran, Martin Cook, Daniel Faria, Nazeefa Fatima, Rob Hooft, Niclas Jareborg, Mijke Jetten, Diana Pilvar, Gil Poires-Oliveira, Marina Popleteeva, Laura Portell-Silva, Jan Slifka, Marek Suchánek, Celia van Gelder, Danielle Welter, Ulrike Wittig, Frederik Coppens, Carole Goble

{"title":"RDMkit: A research data management toolkit for life sciences.","authors":"Pinar Alper, Flora D'Anna, Bert Droesbeke, Munazah Andrabi, Rafael Andrade Buono, Federico Bianchini, Korbinian Bösl, Ishwar Chandramouliswaran, Martin Cook, Daniel Faria, Nazeefa Fatima, Rob Hooft, Niclas Jareborg, Mijke Jetten, Diana Pilvar, Gil Poires-Oliveira, Marina Popleteeva, Laura Portell-Silva, Jan Slifka, Marek Suchánek, Celia van Gelder, Danielle Welter, Ulrike Wittig, Frederik Coppens, Carole Goble","doi":"10.1016/j.patter.2025.101345","DOIUrl":"10.1016/j.patter.2025.101345","url":null,"abstract":"The rise of data-driven scientific investigations has made research data management (RDM) essential for good scientific practice. Implementing RDM is a complex challenge for research communities, infrastructures, and host organizations. Generic RDM guidelines often do not address practical questions, and disciplinary best practices can be overwhelming without proper context. Once guidelines are established, expanding their reach and keeping them up to date is challenging. The RDMkit is an open community-led resource designed as a gateway to reach the wealth of RDM knowledge, tools, training, and resources in life sciences. The RDMkit provides best-practice guidelines on common RDM tasks expected of data stewards and researchers, specific data management challenges and solutions from life science domains, and tool assemblies showcasing holistic solutions to support the research data life cycle. Built on a reusable open infrastructure, the RDMkit allows organizations to create their own guidelines using it as a blueprint.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 9","pages":"101345"},"PeriodicalIF":7.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213814","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

Generating and leveraging explanations of AI/ML models in materials and manufacturing research. 在材料和制造研究中生成和利用AI/ML模型的解释。

IF 7.4

Patterns Pub Date : 2025-08-11 eCollection Date: 2025-09-12 DOI: 10.1016/j.patter.2025.101340

Erick J Braham, Jennifer M Ruddock, James O Hardin

引用次数: 0

Cite what you read, read what you cite. 引用你所读的，读你所引用的。

IF 7.4

Patterns Pub Date : 2025-08-08 DOI: 10.1016/j.patter.2025.101344

Andrew L Hufton

引用次数: 0

Lessons from complex systems science for AI governance. 复杂系统科学对人工智能治理的启示。

IF 7.4

Patterns Pub Date : 2025-08-01 eCollection Date: 2025-08-08 DOI: 10.1016/j.patter.2025.101341

Noam Kolt, Michal Shur-Ofry, Reuven Cohen

{"title":"Lessons from complex systems science for AI governance.","authors":"Noam Kolt, Michal Shur-Ofry, Reuven Cohen","doi":"10.1016/j.patter.2025.101341","DOIUrl":"10.1016/j.patter.2025.101341","url":null,"abstract":"The study of complex adaptive systems, pioneered in physics, biology, and the social sciences, offers important lessons for artificial intelligence (AI) governance. Contemporary AI systems and the environments in which they operate exhibit many of the properties characteristic of complex systems, including nonlinear growth patterns, emergent phenomena, and cascading effects that can lead to catastrophic failures. Complex systems science can help illuminate the features of AI that pose central challenges for policymakers, such as feedback loops induced by training AI models on synthetic data and the interconnectedness between AI systems and critical infrastructure. Drawing on insights from other domains shaped by complex systems, including public health and climate change, we examine how efforts to govern AI are marked by deep uncertainty. To contend with this challenge, we propose three desiderata for designing a set of complexity-compatible AI governance principles comprised of early and scalable intervention, adaptive institutional design, and risk thresholds calibrated to trigger timely and effective regulatory responses.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 8","pages":"101341"},"PeriodicalIF":7.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972451","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

BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models. BioLLM：用于集成和基准测试单细胞基础模型的标准化框架。

IF 7.4

Patterns Pub Date : 2025-07-30 eCollection Date: 2025-08-08 DOI: 10.1016/j.patter.2025.101326

Ping Qiu, Qianqian Chen, Hua Qin, Shuangsang Fang, Yilin Zhang, Yanlin Zhang, Tianyi Xia, Lei Cao, Yong Zhang, Xiaodong Fang, Yuxiang Li, Luni Hu

{"title":"BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models.","authors":"Ping Qiu, Qianqian Chen, Hua Qin, Shuangsang Fang, Yilin Zhang, Yanlin Zhang, Tianyi Xia, Lei Cao, Yong Zhang, Xiaodong Fang, Yuxiang Li, Luni Hu","doi":"10.1016/j.patter.2025.101326","DOIUrl":"10.1016/j.patter.2025.101326","url":null,"abstract":"The application and evaluation of single-cell foundation models (scFMs) present significant challenges due to heterogeneous architectures and coding standards. To address this, we introduce BioLLM (biological large language model), a unified framework for integrating and applying scFMs to single-cell RNA sequencing analysis. BioLLM provides a unified interface that integrates diverse scFMs, eliminating architectural and coding inconsistencies to enable streamlined model access. With standardized APIs and comprehensive documentation, BioLLM supports streamlined model switching and consistent benchmarking. Our comprehensive evaluation of scFMs revealed distinct strengths and limitations, highlighting scGPT's robust performance across all tasks, including zero shot and fine-tuning. Geneformer and scFoundation demonstrated strong capabilities in gene-level tasks, benefiting from effective pretraining strategies. In contrast, scBERT lagged behind, likely due to its smaller model size and limited training data. Ultimately, BioLLM aims to empower the scientific community to leverage the full potential of foundational models, advancing our understanding of complex biological systems through enhanced single-cell analysis.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 8","pages":"101326"},"PeriodicalIF":7.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972404","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

A one-shot, lossless algorithm for cross-cohort learning in mixed-outcomes analysis. 混合结果分析中跨队列学习的一次性无损算法。

IF 7.4

Patterns Pub Date : 2025-07-30 eCollection Date: 2025-09-12 DOI: 10.1016/j.patter.2025.101321

Ruowang Li, Luke Benz, Rui Duan, Joshua C Denny, Hakon Hakonarson, Jonathan D Mosley, Jordan W Smoller, Wei-Qi Wei, Thomas Lumley, Marylyn D Ritchie, Jason H Moore, Yong Chen

{"title":"A one-shot, lossless algorithm for cross-cohort learning in mixed-outcomes analysis.","authors":"Ruowang Li, Luke Benz, Rui Duan, Joshua C Denny, Hakon Hakonarson, Jonathan D Mosley, Jordan W Smoller, Wei-Qi Wei, Thomas Lumley, Marylyn D Ritchie, Jason H Moore, Yong Chen","doi":"10.1016/j.patter.2025.101321","DOIUrl":"10.1016/j.patter.2025.101321","url":null,"abstract":"In cross-cohort studies, integrating diverse datasets is essential and challenging due to cohort-specific variations, distributed data storage, and privacy concerns. Traditional methods often require data pooling or harmonization, which can reduce efficiency and limit the scope of cross-cohort learning. We introduce mixWAS, a one-shot, lossless algorithm that efficiently integrates distributed electronic health record (EHR) datasets via summary statistics. Unlike existing approaches, mixWAS preserves cohort-specific covariate associations and supports simultaneous mixed-outcome analyses. Simulations demonstrate that mixWAS outperforms conventional methods in accuracy and efficiency across various scenarios. Applied to EHR data from seven cohorts in the US, mixWAS identified 4,530 significant cross-cohort genetic associations among traits such as blood lipids, BMI, and circulatory diseases. Validation with an independent UK EHR dataset confirmed 97.7% of these associations, underscoring the algorithm's robustness. By enabling lossless cross-cohort integration, mixWAS improves the precision of multi-outcome analyses and expands the potential for actionable insights in healthcare research.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 9","pages":"101321"},"PeriodicalIF":7.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214044","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

Pyomo: Accidentally outrunning the bear. Pyomo：不小心跑过了熊。

IF 7.4

Patterns Pub Date : 2025-07-11 DOI: 10.1016/j.patter.2025.101311

Miranda Mundt, William E Hart, Emma S Johnson, Bethany Nicholson, John D Siirola

引用次数: 0

A conversation with research software engineers at the International Brain Laboratory. 与国际大脑实验室研究软件工程师的对话。

IF 7.4

Patterns Pub Date : 2025-07-11 DOI: 10.1016/j.patter.2025.101315

Mayo Faulkner, Miles Wells

引用次数: 0