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arXiv 2024年

作者： Liu, Jiangfeng Wang, Ziyi Xie, Jing Pei, Lei Data Intelligence and Cross-Innovation Laboratory Nanjing University Nanjing210023 China School of Information Management Nanjing University Nanjing210023 China School of Health Economics and Management Nanjing University of Chinese Medicine Nanjing210023 China

Generative large-scale language models create the fifth paradigm of scientific research, organically combine data science and computational intelligence, transform the research paradigm of natural language processing and multimodal information processing, promote the new trend of AI-enabled social science research, and provide new ideas for digital humanities research and application. This article profoundly explores the application of large-scale language models in digital humanities research, revealing their significant potential in ancient book protection, intelligent processing, and academic innovation. The article first outlines the importance of ancient book resources and the necessity of digital preservation, followed by a detailed introduction to developing large-scale language models, such as ChatGPT, and their applications in document management, content understanding, and cross-cultural research. Through specific cases, the article demonstrates how AI can assist in the organization, classification, and content generation of ancient books. Then, it explores the prospects of AI applications in artistic innovation and cultural heritage preservation. Finally, the article explores the challenges and opportunities in the interaction of technology, information, and society in the digital humanities triggered by AI technologies. © 2024, CC BY-NC-ND.

关键词： Digital storage

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Artificial intelligence for Central Dogma-Centric Multi-Omics: Challenges and Breakthroughs

arXiv

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arXiv 2024年

作者： Xin, Lei Huang, Caiyun Li, Hao Huang, Shihong Feng, Yuling Kong, Zhenglun Liu, Zicheng Li, Siyuan Yu, Chang Shen, Fei Tang, Hao School of Computer Science Peking University Beijing100871 China Wuhan University Hubei Wuhan430072 China Interdisciplinary Institute of Life Medicine Hunan University Hunan Changsha410082 China Laboratory of Data Intelligence and Cross Innovation Nanjing University Jiangsu Nanjing210023 China School of Information Management Nanjing University Jiangsu Nanjing210023 China College of Information Management Nanjing Agricultural University Jiangsu Nanjing210095 China Faculty of Science Nanjing Agricultural University Jiangsu Nanjing210095 China Department of Pharmacology University of Michigan Medical School Ann ArborMI48109 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States AI Lab Research Center for Industries of the Future Westlake University Zhejiang Hangzhou310058 China Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology Institute of Biotechnology Beijing Academy of Agriculture and Forestry Sciences Beijing China

With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics, metabolomics, and transcriptomics, are being applied to disease genetics research. However, biological data often exhibit high dimensionality and significant noise, making it challenging to effectively distinguish disease subtypes using a single-omics approach. To address these challenges and better capture the interactions among DNA, RNA, and proteins described by the central dogma, numerous studies have leveraged artificial intelligence to develop multi-omics models for disease research. These AI-driven models have improved the accuracy of disease prediction and facilitated the identification of genetic loci associated with diseases, thus advancing precision medicine. This paper reviews the mathematical definitions of multi-omics, strategies for integrating multi-omics data, applications of artificial intelligence and deep learning in multi-omics, the establishment of foundational models, and breakthroughs in multi-omics technologies, drawing insights from over 130 related articles. It aims to provide practical guidance for computational biologists to better understand and effectively utilize AI-based multi-omics machine learning algorithms in the context of central dogma. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Global tracking of marine megafauna space use reveals how to achieve conservation targets

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Science (New York, N.Y.) 2025年第6751期388卷 1086-1097页

作者： Ana M M Sequeira Jorge P Rodríguez Sarah A Marley Hannah J Calich Mirjam van der Mheen Michelle VanCompernolle Lucy M Arrowsmith Lauren R Peel Nuno Queiroz Marisa Vedor Ivo da Costa Gonzalo Mucientes Ana Couto Nicolas E Humphries Sara Abalo-Morla Francisco J Abascal Debra L Abercrombie Katya Abrantes F Alberto Abreu-Grobois André S Afonso Pedro Afonso Heidi Ahonen Susanne Åkesson Joanna Alfaro-Shigueto Russel D Andrews Frédéric Angelier Marina Antonopoulou Javier A Arata Gonzalo Araujo Randall Arauz José Manuel Arcos Igor Arregui Haritz Arrizabalaga Marie Auger-Méthé Steffen Bach Fred Bailleul Robin W Baird George H Balazs Susan G Barco Adam Barnett Warren Baverstock Alastair M M Baylis Annalea Beard Juan Bécares Eduardo J Belda Ian Bell Ashley Bennison Scott R Benson Diego Bernal Michael L Berumen Sandra Bessudo Natalia P A Bezerra Antonin V Blaison Gabriela S Blanco Barbara A Block Mark Bolton Mark E Bond Ramón Bonfil Camrin D Braun Annette C Broderick Michael de L Brooke Annabelle M L Brooks Edward J Brooks Ignacio M Bruno Jennifer M Burns Michael E Byrne Steven E Campana Hamish A Campbell Richard A Campbell Aaron Carlisle Ruth H Carmichael Gemma Carroll Paolo Casale Filipe R Ceia Demian D Chapman Taylor K Chapple Jean-Benoit Charrassin Andre Chiaradia John Chisholm Christopher R Clarke Thomas A Clay Christophe Cleguer Elizabeth Clingham Eric E G Clua Jesse E M Cochran Rochelle Constantine Robert W Cooper Estelle Crochelet Michelle Cronin Eduardo Cuevas Kayla P DaCosta Laurent Dagorn Ryan Daly Randall W Davis P J Nico de Bruyn Carlos Delgado-Trejo Thomas Dellinger Solène Derville Stella Diamant Andrew DiMatteo Kara L Dodge Philip D Doherty Michael C Double Alistair D M Dove Thomas K Doyle Michael J Drew Lindsay L Dubbs Clinton A J Duffy Peter H Dutton Ewan W J Edwards Luke D Einoder Mark V Erdmann Eduardo Espinoza Nicole Esteban Ana Isabel Fagundes Chris Feare Steven H Ferguson Luciana C Ferreira Francesco Ferretti John Filmalter Brittany Finucci G Chris Fischer Richard J Fitzpatrick Jorge Fontes Angela Formia Division of Ecology and Evolution Research School of Biology The Australian National University Canberra (Australian Capital Territory) Australia. UWA Oceans Institute and the School of Biological Sciences The University of Western Australia Perth (Western Australia) Australia. Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) CSIC-UIB Palma de Mallorca Spain. Instituto Mediterráneo de Estudios Avanzados (IMEDEA) CSIC-UIB Esporles Illes Balears Spain. C.A. Illes Balears Universidad Nacional de Educación a Distancia (UNED) Palma Spain. Delegación Territorial en Illes Balears Agencia Estatal de Meteorología (AEMET) Muelle de Poniente s/n Palma Spain. Scotland's Rural College (SRUC) Craibstone Estate Bucksburn Aberdeen UK. Oceans Graduate School and the UWA Oceans Institute The University of Western Australia Perth (Western Australia) Australia. School of Engineering and the UWA Oceans Institute The University of Western Australia Perth (Western Australia) Australia. Department of Biodiversity Conservation and Attractions Biodiversity and Conservation Science Kensington (Western Australia) Australia. Save Our Seas Foundation Geneva Switzerland. The Manta Trust Catemwood House Norwood Lane Corscombe Dorset UK. Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO) InBIO Laboratório Associado Campus de Vairão Universidade do Porto Vairão Portugal. BIOPOLIS Program in Genomics Biodiversity and Land Planning Campus de Vairao Vairão Portugal. Instituto de Investigaciones Marinas Consejo Superior de Investigaciones Científicas (IIM-CSIC) Vigo Pontevedra Spain. School of Biological Sciences University of Aberdeen Aberdeen UK. Biomathematics and statistics Scotland The kings building Edinburgh Scotland. Marine Biological Association (MBA) The Laboratory Citadel Hill Plymouth UK. Instituto de Investigación para la Gestión Integrada de Zonas Costeras Universitat Politècnica de València Gandia València Spain. Instituto Español de O

The recent Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals but no clear pathway for how zero loss of important biodiversity areas and halting human-induced extinction of threatened species will be achieved. We assembled a multi-taxa tracking dataset (11 million geopositions from 15,845 tracked individuals across 121 species) to provide a global assessment of space use of highly mobile marine megafauna, showing that 63% of the area that they cover is used 80% of the time as important migratory corridors or residence areas. The GBF 30% threshold (Target 3) will be insufficient for marine megafauna's effective conservation, leaving important areas exposed to major anthropogenic threats. Coupling area protection with mitigation strategies (e.g., fishing regulation, wildlife-traffic separation) will be essential to reach international goals and conserve biodiversity.

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Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci (vol 50, pg 928, 2018)

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NATURE GENETICS 2019年第2期51卷 363-363页

作者： Schumacher, Fredrick R. Al Olama, Ali Amin Berndt, Sonja I. Benlloch, Sara Ahmed, Mahbubl Saunders, Edward J. Dadaev, Tokhir Leongamornlert, Daniel Anokian, Ezequiel Cieza-Borrella, Clara Goh, Chee Brook, Mark N. Sheng, Xin Fachal, Laura Dennis, Joe Tyrer, Jonathan Muir, Kenneth Lophatananon, Artitaya Stevens, Victoria L. Gapstur, Susan M. Carter, Brian D. Tangen, Catherine M. Goodman, Phyllis J. Thompson, Ian M., Jr. Batra, Jyotsna Chambers, Suzanne Moya, Leire Clements, Judith Horvath, Lisa Tilley, Wayne Risbridger, Gail P. Gronberg, Henrik Aly, Markus Nordstrom, Tobias Pharoah, Paul Pashayan, Nora Schleutker, Johanna Tammela, Teuvo L. J. Sipeky, Csilla Auvinen, Anssi Albanes, Demetrius Weinstein, Stephanie Wolk, Alicja Hakansson, Niclas West, Catharine M. L. Dunning, Alison M. Burnet, Neil Mucci, Lorelei A. Giovannucci, Edward Andriole, Gerald L. Cussenot, Olivier Cancel-Tassin, Geraldine Koutros, Stella Freeman, Laura E. Beane Sorensen, Karina Dalsgaard Orntoft, Torben Falck Borre, Michael Maehle, Lovise Grindedal, Eli Marie Neal, David E. Donovan, Jenny L. Hamdy, Freddie C. Martin, Richard M. Travis, Ruth C. Key, Tim J. Hamilton, Robert J. Fleshner, Neil E. Finelli, Antonio Ingles, Sue Ann Stern, Mariana C. Rosenstein, Barry S. Kerns, Sarah L. Ostrer, Harry Lu, Yong-Jie Zhang, Hong-Wei Feng, Ninghan Mao, Xueying Guo, Xin Wang, Guomin Sun, Zan Giles, Graham G. Southey, Melissa C. MacInnis, Robert J. FitzGerald, Liesel M. Kibel, Adam S. Drake, Bettina F. Vega, Ana Gomez-Caamano, Antonio Szulkin, Robert Eklund, Martin Kogevinas, Manolis Llorca, Javier Castano-Vinyals, Gemma Penney, Kathryn L. Stampfer, Meir Park, Jong Y. Sellers, Thomas A. Lin, Hui-Yi Stanford, Janet L. Cybulski, Cezary Wokolorczyk, Dominika Lubinski, Jan Ostrander, Elaine A. Geybels, Milan S. Nordestgaard, Borge G. Nielsen, Sune F. Weischer, Maren Bisbjerg, Rasmus Roder, Martin Andreas Iversen, Peter Brenner, Hermann Cuk, Katarina Holleczek, Bernd Maier, Christiane Luedeke, Manuel Schnoeller, Thomas Kim, Jeri Logothetis, Christopher J. John, E Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland USA Seidman Cancer Center University Hospitals Cleveland USA Royal Marsden NHS Foundation Trust London UK Department of Public Health and Primary Care Centre for Cancer Genetic Epidemiology University of Cambridge Strangeways Research Laboratory Cambridge UK Department of Clinical Neurosciences University of Cambridge Cambridge UK Division of Cancer Epidemiology and Genetics National Cancer Institute NIH Bethesda USA Institute of Cancer Research London UK Department of Preventive Medicine Keck School of Medicine University of Southern California/Norris Comprehensive Cancer Center Los Angeles USA Fundación Pública Galega de Medicina Xenómica–SERGAS Grupo de Medicina Xenómica CIBERER IDIS Santiago de Compostela Spain Department of Oncology Centre for Cancer Genetic Epidemiology Strangeways Laboratory University of Cambridge Cambridge UK Division of Population Health Health Services Research and Primary Care University of Manchester Manchester UK Warwick Medical School University of Warwick Coventry UK Epidemiology Research Program American Cancer Society Atlanta USA SWOG Statistical Center Fred Hutchinson Cancer Research Center Seattle USA CHRISTUS Santa Rosa Hospital–Medical Center San Antonio USA Institute of Health and Biomedical Innovation and School of Biomedical Sciences Queensland University of Technology Brisbane Australia Australian Prostate Cancer Research Centre–Qld Translational Research Institute Brisbane Australia Menzies Health Institute of Queensland Griffith University Nathan Australia Cancer Council Queensland Fortitude Valley Australia Chris O’Brien Lifehouse (COBLH) Camperdown Australia Garvan Institute of Medical Research Sydney Australia Dame Roma Mitchell Cancer Research Centre University of Adelaide Adelaide Australia Monash Biomedicine Discovery Institute Cancer Program Prostate Cancer Research Program Department of Anatom

In the version of this article initially published, the name of author Manuela Gago-Dominguez was misspelled as Manuela Gago Dominguez. The error has been corrected in the HTML and PDF version of the article.

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