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Hybrid Advances 2024年 6卷

作者： Alli, Yakubu Adekunle Anuar, Hazleen Manshor, Mohd Romainor Okafor, Christian Emeka Kamarulzaman, Amjad Fakhri Akçakale, Nürettin Mohd Nazeri, Fatin Nurafiqah Bodaghi, Mahdi Suhr, Jonghwan Mohd Nasir, Nur Aimi Department of Manufacturing and Materials Engineering Kulliyyah of Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur 53100 Malaysia CNRS LCC (Laboratoire de Chimie de Coordination) UPR8241 Université de Toulouse UPS INPT Toulouse cedex 4 F-31077 France Department of Chemical Sciences Faculty of Science and Computing Ahman Pategi University Km 3 Patigi-Kpada Road Kwara State Patigi Nigeria Food Sciences & Technology Research Center Malaysian Agricultural Research and Development Institute Persiaran MARDI-UPM Selangor Serdang 43400 Malaysia Department of Mechanical Engineering Nnamdi Azikiwe University Awka Nigeria Textile Clothing Footwear and Leather Department Gerede Vocational School of Higher Education Abant İzzet Baysal University Bolu 14900 Turkey Department of Engineering School of Science and Technology Nottingham Trent University Nottingham NG11 8NS United Kingdom Faculty of Mechanical Engineering College of Engineering Sungkyunkwan University Natural Sciences Campus 2066 Seobu-ro Jangan-gu Gyeonggi-do Suwon 16419 South Korea NanoVerify Sdn Bhd e-8-6 (Suite 5.8) Block E Megan Avenue 1 189 Jalan Tun Razak Kuala Lumpur 54050 Malaysia

This systematic review explores the integration of 4D/3D printing technologies with machine learning, shaping a new era of manufacturing innovation. The analysis covers a wide range of research papers, articles, and patents, presenting a multidimensional perspective on the advancements in additive manufacturing. The review underscores machine learning's pivotal role in optimizing 4D/3D printing, addressing aspects like design customization, material selection, process control, and quality assurance. The examination reveals novel techniques enabling the fabrication of intelligent, self-adaptive structures capable of transformation over time. Additionally, the review investigates the use of predictive algorithms to enhance efficiency, reliability, and sustainability in 4D/3D printing processes. Applications span aerospace, healthcare, architecture, and consumer goods, showcasing the potential to create intricate, personalized, and once-unattainable functional products. The synergy between machine learning and 4D/3D printing is poised to unlock new manufacturing horizons, enabling rapid responses to market demands and sustainability challenges. In summary, this review provides a comprehensive overview of the current state of 4D/3D printing optimization through machine learning, highlighting the transformative potential of this interdisciplinary fusion and offering a roadmap for future research and development. It aims to inspire innovators, researchers, and industries to harness this powerful combination for accelerated evolution in manufacturing processes into the 21st century and beyond. © 2024 The Authors

关键词： 3D printing 4D printing Machine learning Smart materials

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Thalamocortical Connection Topography Mapping in Human by Co-clustering

Thalamocortical Connection Topography Mapping in Human by Co...

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2019 3rd International Conference on Machine Vision and Information Technology (CMVIT 2019)

作者： Hewei Cheng Jie Liu Yiying Tao Department of Biomedical Engineering School of Bioinformatics Chongqing University of Posts and Telecommunications Center for Biomedical Image Computing and Analytics Department of Radiology Perelman School of Medicine University of Pennsylvania Research Institute of Education Development Chongqing University of Posts and Telecommunications

Connection topography mapping is crucial for understanding how information is processed in the brain, which is an essential precursor for revealing principles of brain organization. However, existing connectopic mapping methods are dependent on prior knowledge, or not completely driven by data. Accordingly, the constructed connection topographies by these methods are biased towards hypotheses, or deviate from data. For these challenges, we propose a novel co-clustering based method for connection topography mapping in a fully data-driven manner. The proposed method aims to construct the connection topography between two ROIs of a certain neural circuit in consideration by leveraging the power of co-clustering. More precisely, the proposed method parcellates one ROI into subregions and identified their respective connected subregions from the other ROI simultaneously. The effectiveness of our method was validated on the mapping of the human thalamocortical system for 57 subjects based on their resting state f MRI data. The validation experiment results have demonstrated that our method can construct neurobiologically meaningful thalamocortical connection topography. Compared with existing methods, our method yields more meaningful and interpretable connection topography.

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Corrigendum to “Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization” [Eng. Appl. Artif. Intell. 124 (2023) 106643]

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Engineering Applications of Artificial Intelligence 2024年 128卷

作者： A.A. Zaidan Alhamzah Alnoor O.S. Albahri R.T. Mohammed A.H. Alamoodi A.S. Albahri B.B. Zaidan Salem Garfan Hamsa Hameed Mohammed S. Al-Samarraay Ali Najm Jasim R.Q. Malik SP Jain School of Global Management Sydney Australia Management Technical College Southern Technical University Basrah Iraq Department of Computer Science and Information Technology La Trobe University Melbourne VIC Australia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Science Faculty of Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq Department of Computing and META Technology (FKMT) Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia MEU Research Unit Middle East University Amman Jordan Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Faculty of Human Development Sultan Idris University of Education (UPSI) Tanjung Malim Malaysia Foundation of Alshuhda Baghdad Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

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MASTREE+: Time-series of plant reproductive effort from six continents

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Global Change Biology 2022年第9期28卷 3066-3082页

作者： Hacket-Pain, Andrew Foest, Jessie J. Pearse, Ian S. LaMontagne, Jalene M. Koenig, Walter D. Vacchiano, Giorgio Bogdziewicz, Michał Caignard, Thomas Celebias, Paulina van Dormolen, Joep Fernández-Martínez, Marcos Moris, Jose V. Palaghianu, Ciprian Pesendorfer, Mario Satake, Akiko Schermer, Eliane Tanentzap, Andrew J. Thomas, Peter A. Vecchio, Davide Wion, Andreas P. Wohlgemuth, Thomas Xue, Tingting Abernethy, Katharine Aravena Acuña, Marie-Claire Daniel Barrera, Marcelo Barton, Jessica H. Boutin, Stan Bush, Emma R. Donoso Calderón, Sergio Carevic, Felipe S. de Castilho, Carolina Volkmer Manuel Cellini, Juan Chapman, Colin A. Chapman, Hazel Chianucci, Francesco da Costa, Patricia Croisé, Luc Cutini, Andrea Dantzer, Ben Justin DeRose, R. Dikangadissi, Jean-Thoussaint Dimoto, Edmond da Fonseca, Fernanda Lopes Gallo, Leonardo Gratzer, Georg Greene, David F. Hadad, Martín A. Herrera, Alejandro Huertas Jeffery, Kathryn J. Johnstone, Jill F. Kalbitzer, Urs Kantorowicz, Władysław Klimas, Christie A. Lageard, Jonathan G. A. Lane, Jeffrey Lapin, Katharina Ledwoń, Mateusz Leeper, Abigail C. Vanessa Lencinas, Maria Lira-Guedes, Ana Cláudia Lordon, Michael C. Marchelli, Paula Marino, Shealyn Schmidt Van Marle, Harald McAdam, Andrew G. Momont, Ludovic R. W. Nicolas, Manuel de Oliveira Wadt, Lúcia Helena Panahi, Parisa Martínez Pastur, Guillermo Patterson, Thomas Luis Peri, Pablo Piechnik, Łukasz Pourhashemi, Mehdi Espinoza Quezada, Claudia Roig, Fidel A. Peña Rojas, Karen Micaela Rosas, Yamina Schueler, Silvio Seget, Barbara Soler, Rosina Steele, Michael A. Toro-Manríquez, Mónica Tutin, Caroline E. G. Ukizintambara, Tharcisse White, Lee Yadok, Biplang Willis, John L. Zolles, Anita Żywiec, Magdalena Ascoli, Davide Department of Geography and Planning School of Environmental Sciences University of Liverpool Liverpool United Kingdom U.S. Geological Survey Fort Collins Science Center Fort Collins CO United States Department of Biological Sciences DePaul University Chicago IL United States Hastings Reservation University of California Berkeley Carmel Valley CA United States Department of Agricultural and Environmental Sciences University of Milan Milan Italy Faculty of Biology Institute of Environmental Biology Adam Mickiewicz University Poznań Poland INRAE LESSEM University Grenoble Alpes Grenoble France Univ. Bordeaux INRAE BIOGECO Pessac France Department of Computing University of London London United Kingdom CREAF Catalonia Barcelona Spain Department of Agricultural Forest and Food Sciences (DISAFA) University of Torino Torino Italy Stefan cel Mare Univ Suceava Forestry Fac Appl Ecol Lab Suceava Romania Department of Forest and Soil Sciences Institute of Forest Ecology University of Natural Resources and Life Sciences Vienna Vienna Austria Kyushu University Fukuoka Japan Aix Marseille Univ Avignon Université CNRS IRD IMBE Marseille France Ecosystems and Global Change Group Department of Plant Sciences University of Cambridge Cambridge United Kingdom School of Life Sciences Keele University Staffordshire United Kingdom Graduate Degree Program in Ecology and The Department of Forest and Rangeland Stewardship Colorado State University Fort Collins CO United States Swiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland College of Civil and Architecture and Engineering Chuzhou University China Faculty of Natural Sciences University of Stirling Stirling United Kingdom Institut de Recherche en Ecologie Tropicale CENAREST Libreville Gabon Facultad de Ciencias Forestales y de la Conservación de la Naturaleza (FCFCN) Universidad de Chile Santiago Chile Universidad Nacional de la Plata (UNLP) Buenos Aires Argentina

Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long-lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time-series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide. These observations consist of 5971 population-level time-series from 974 species in 66 countries. The mean and median time-series length is 12.4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (≥20 years of observations). For a subset of well-studied species, MASTREE+ includes extensive replication of time-series across geographical and climatic gradients. Here we describe the open-access data set, available as *** file, and we introduce an associated web-based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long-lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.

关键词： demography flowering general flowering masting plant reproduction recruitment regeneration

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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An IoT Edge computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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IEEE International Conference on Networking, Sensing and Control

作者： Shichao Chen Qijie Li Hua Zhang Fenghua Zhu Gang Xiong Ying Tang Macau University of Science and Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Macau China Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Engineering Research Center of Intelligent Systems and Technology Institute of Automation and Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Glassboro NJ USA Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT.

关键词： Energy consumption Processor scheduling Computer architecture Internet of Things Optimization Monitoring Edge computing

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Graphical representation and hierarchical decomposition mechanism for vertex-cover solution space

arXiv

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arXiv 2019年

作者： Wei, Wei Feng, Xiangnan Wang, Jiannan Liu, Xue Zheng, Zhiming School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education 100191 China Research Institute of Frontier Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Center for Humans and Machines Max Planck Institute for Human Development Berlin14195 Germany

In this paper, solution space organization of minimum vertex-cover problem is deeply investigated using the König-Egérvary (KE) graph and theorem, in which a hierarchical decomposition mechanism named KE-layer structure of general graphs is proposed to reveal the complexity of vertex-cover. An algorithm to verify the KE graph is given by the solution space expression of vertex-cover, and the relation between multi-layer KE graphs and maximal matching is illustrated and proved. Furthermore, a framework to calculate the KE-layer number and approximate the minimal vertex-cover is provided, with different strategies of switching nodes and counting energy. The phase transition phenomenon between different KE-layers are studied with the transition points located, and searching of vertex-cover got by this strategy presents comparable advantage against several other methods. The graphical representation and hierarchical decomposition provide a new perspective to illustrate the intrinsic complexity for large-scale graphs/systems recognition. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis

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Neuroscience and Biobehavioral Reviews 2025年 174卷 106201页

作者： Zhang, Lin Jian, Liwen Long, Yiming Ren, Zhihong Calhoun, Vince D. Passos, Ives Cavalcante Tian, Xinyu Xiang, Yuhong School of Psychology Central China Normal University Wuhan 430079 China Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU) Ministry of Education Wuhan 430079 China Key Laboratory of Human Development and Mental Health of Hubei Province School of Psychology Central China Normal University Wuhan 430079 China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Klaus Advanced Computing Building 266 Ferst Drive Atlanta 30332-0765 Georgia Department of Psychiatry School of Medicine Universidade Federal do Rio Grande do Sul RS Porto Alegre Brazil Laboratory of Molecular Psychiatry Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC) Hospital de Clínicas de Porto Alegre (HCPA) RS Porto Alegre Brazil Department of Psychiatry School of Medicine Universidade Federal do Rio Grande do Sul R. Ramiro Barcelos 2400 Floresta RS Porto Alegre 90035002 Brazil

Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contrast, machine learning models have the potential to classify and diagnose MDD more effectively, reducing the risk of misdiagnosis associated with conventional methods. The aim of this meta-analysis is to evaluate the overall classification accuracy of machine learning models in MDD and examine the effects of machine learning algorithms, biomarkers, diagnostic comparison groups, validation procedures, and participant age on classification performance. As of September 2024, a total of 176 studies were ultimately included in the meta-analysis, encompassing a total of 60,926 participants. A random-effects model was applied to analyze the extracted data, resulting in an overall classification accuracy of 0.825 (95 % CI [0.810;0.839]). Convolutional neural networks significantly outperformed support vector machines (SVM) when using electroencephalography and magnetoencephalography data. Additionally, SVM demonstrated significantly better performance with functional magnetic resonance imaging data compared to graph neural networks and gaussian process classification. The sample size was negatively correlated to classification accuracy. Furthermore, evidence of publication bias was also detected. Therefore, while this study indicates that machine learning models show high accuracy in distinguishing MDD from healthy controls and other psychiatric disorders, further research is required before these findings can be generalized to large-scale clinical practice. © 2025 Elsevier Ltd

关键词： Big data Biomarkers Machine Learning Major Depressive Disorder

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