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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

A quantum annealing approach to ionic diffusion in solids

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arXiv 2019年

作者： Utimula, Keishu Ichibha, Tom Prayogo, Genki I. Hongo, Kenta Nakano, Kousuke Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

We have developed a framework for using quantum annealing computation to evaluate a key quantity in ionic diffusion in solids, the correlation factor. Existing methods can only calculate the correlation factor analytically in the case of physically unrealistic models, making it difficult to relate microstructural information about diffusion path networks obtainable by current ab initio techniques to macroscopic quantities such as diffusion coefficients. We have mapped the problem into a quantum spin system described by the Ising Hamiltonian. By applying our framework in combination with ab initio technique, it is possible to understand how diffusion coefficients are controlled by temperatures, pressures, atomic substitutions, and other factors. We have calculated the correlation factor in a simple case with a known exact result by a variety of computational methods, including simulated quantum annealing on the spin models, the classical random walk, the matrix description, and quantum annealing on D-Wave with hybrid solver. This comparison shows that all the evaluations give consistent results with each other, but that many of the conventional approaches require infeasible computational costs. Quantum annealing is also currently infeasible because of the cost and scarcity of Q-bits, but we argue that when technological advances alter this situation, quantum annealing will easily outperform all existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Hamiltonians

Stochastic estimations of a total number of classes for the clusterings with too enormous samples to be accommodate into a clustering engine

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arXiv 2019年

作者： Utimula, Keishu Prayogo, Genki I. Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total number of the possibility. Concrete situations are explained taking examples of atomic substitutions in the supercell modeling of alloys. The number of the possibility sometimes amounts to ∼ trillion being too large to be accommodate. It is hence not practically feasible to identify how many irreducible classes exist by straightforward manner even though there are several tools available to perform the clustering. We have developed a stochastic framework to avoid the shortage of capacity, providing a method to estimate the total number of irreducible classes (the order of the classes) as a statistical estimate. A prominent conclusion derived here is that the statistical variation of the number of classes at each sampling trial is working as a promising measure to estimate the order. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

The putative center in NGC 1052

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arXiv 2025年

作者： Baczko, Anne-Kathrin Kadler, Matthias Ros, Eduardo Fromm, Christian M. Wielgus, Maciek Perucho, Manel Krichbaum, Thomas P. Baloković, Mislav Blackburn, Lindy Chan, Chi-Kwan Issaoun, Sara Janssen, Michael Ricci, Luca Akiyama, Kazunori Albentosa-Ruíz, Ezequiel Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Ball, David Bandyopadhyay, Bidisha Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brinkerink, Christiaan D. Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bronzwaer, Thomas Bustamante, Sandra Byun, Do-Young Carlstrom, John E. Ceccobello, Chiara Chael, Andrew Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Cordes, James M. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu Dahale, Rohan Davelaar, Jordy De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dexter, Jason Dhruv, Vedant Dihingia, Indu K. Doeleman, Sheperd S. Dougall, Sean Taylor Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Edward Ford, H. Alyson Foschi, Marianna Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fuentes, Antonio Galison, Peter Gammie, Charles F. García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco Gold, Roman Gómez-Ruiz, Arturo I. Gómez, José L. Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Haworth, Kari Hecht, Michael H. Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Impellizzeri, C.M. Violette Inoue, Makoto James, David J. Jannuzi, Buell T. Jeter, Britton Jiang, Wu Jiménez-Rosales, Alejandra Johnson, Michael D. Jorstad, Svetlana Joshi, Abhishek V. Jung, Taehyun Karami, Mansour Karuppusamy, Ramesh Kawashima, Tomohisa Keating, Garrett K. Kettenis, M Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-41296 Sweden Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Institut für Theoretische Physik und Astrophysik Universität Würzburg Emil-Fischer-Str. 31 WürzburgD-97074 Germany Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Departament d'Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 Paterna ValènciaE-46980 Spain Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New HavenCT06511 United States Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Radboud University PO Box 9010 Nijmegen6500 GL Netherlands Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Astronomy Depart

Context. Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. Aims. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. Methods. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. Our study is focused on the size and continuum spectrum of the innermost region containing the central engine and the footpoints of both jets. We employed a synchrotron-self absorption model to fit the continuum radio spectrum and we combined the size measurements from close to the central engine out to ∼1 pc to study the jet collimation. Results. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 43 µas perpendicular to the jet axes, corresponding to just around 250 RS (Schwarzschild radii). This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 3 × 103 RS and 1 × 104 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 µas from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at ∼130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 2.6 × 104 Gauss, which is consistent with previous measurements. Conclusions. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dens

关键词： Synchrotrons

Author Correction: Inferring structural variant cancer cell fraction

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Nature communications 2022年第1期13卷 7568页

作者： Marek Cmero Ke Yuan Cheng Soon Ong Jan Schröder Niall M Corcoran Tony Papenfuss Christopher M Hovens Florian Markowetz Geoff Macintyre Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. cmerom@unimelb.edu.au. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. cmerom@unimelb.edu.au. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. cmerom@unimelb.edu.au. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. cmerom@unimelb.edu.au. Murdoch Children's Research Institute Parkville VIC 3052 Australia. cmerom@unimelb.edu.au. School of Computing Science University of Glasgow Sir Alwyn Williams Building Glasgow G12 8RZ UK. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. Electrical and Electronic Engineering University of Melbourne Parkville VIC 3010 Australia. Machine Learning Research Group Data61 Canberra ACT 2601 Australia. Research School of Computer Science Australian National University Canberra ACT 2601 Australia. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. geoff.macintyre@cruk.cam.ac.uk. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. geoff.macintyre@cruk.cam.ac.uk.

ICTNET at TREC 2017 Dynamic Domain Track 26

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ICTNET at TREC 2017 Dynamic Domain Track

26th Text REtrieval Conference, TREC 2017

作者： Zhang, Weimin Hu, Yaokang Jia, Rongqian Wang, Xianfa Zhang, Le Feng, Yue Yu, Sihao Xue, Yuanhai Yu, Xiaoming Liu, Yue Cheng, Xueqi Institute of Computing Technology CAS China Key Laboratory of Web Data Science and Technology CAS China University of Chinese Academy of Sciences China

Cometary Activity Discovered on Vacationing Centaur 2019 OE31

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Research Notes of the AAS 2023年第12期7卷 270-270页

作者： William J. Oldroyd Colin Orion Chandler Chadwick A. Trujillo Sam Deen Henry H. Hsieh Kennedy A. Farrell Jarod A. DeSpain Jay K. Kueny William A. Burris Scott S. Sheppard Nima Sedaghat Mark Jesus Mendoza Magbanua Michele T. Mazzucato Milton K. D. Bosch Tiffany Shaw-Diaz Virgilio Gonano Al Lamperti José A. da Silva Campos Brian L. Goodwin Ivan A. Terentev Charles J. A. Dukes Dept. of Astronomy & Planetary Science Northern Arizona University P.O. Box 6010 Flagstaff AZ 86011 USA woldroyd@nau.edu Dept. of Astronomy & the DiRAC Institute University of Washington 3910 15th Ave NE Seattle WA 98195 USA LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue Tucson AZ 85721 USA Raw Data Speaks Initiative Active Asteroids Citizen Scientist Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 Tucson AZ 85719 USA Institute of Astronomy and Astrophysics Academia Sinica P.O. Box 23-141 Taipei 10617 Taiwan Dept. of Astronomy and Steward Observatory University of Arizona 933 North Cherry Avenue Rm. N204 Tucson AZ 85721 USA Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road. NW Washington DC 20015 USA Dept. of Laboratory Medicine University of California San Francisco 2340 Sutter Street San Francisco CA 94143 USA Royal Astronomical Society Burlington House Piccadilly London W1J 0BQ UK Physical Sciences Group Siena Academy of Sciences Piazzetta Silvio Gigli 2 I-53100 Siena Italy Delaware Valley Amateur Astronomers 112 Pebble Beach Drive Royersford PA 19468 USA Astronomical Society of Southern Africa P.O. Box 9 Observatory 7935 Cape Town South Africa

We have detected cometary activity on minor planet 2019 OE31 through both the Active Asteroids Citizen science program and an independent archival search. Before 2013, 2019 OE31 was on a Centaur orbit, between the orb...

We have detected cometary activity on minor planet 2019 OE₃₁ through both the Active Asteroids Citizen science program and an independent archival search. Before 2013, 2019 OE₃₁ was on a Centaur orbit, between the orbits of Jupiter and Neptune. Centaurs are objects in transition from the outer solar system to the inner solar system. They play a vital role in the understanding of the Kuiper Belt and comets. In 2013 October, following a close encounter with Jupiter, 2019 OE₃₁ moved to an orbit entirely interior to that of Jupiter. This reduced orbital distance and, hence, increased temperature is likely the cause of the observed activity. Through a suite of orbital dynamics simulations, we find that 2019 OE₃₁ will experience many more similar encounters and is statistically likely to return to a Centaur orbit, potentially within the next 80 yr, from its current "vacation."

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τ-FPL: Tolerance-constrained learning in linear time

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arXiv 2018年

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research - China Shanghai China College of Computing Georgia Institute of Technology AtlantaGA United States

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, τ -False Positive Learning (τ - FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ -FPL over existing approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Mathematical transformations

On the relationship between energy complexity and other boolean function measures

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arXiv 2018年

作者： Sun, Xiaoming Sun, Yuan Wu, Kewen Xia, Zhiyu CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineering and Computer Science Peking University China

In this work we investigate energy complexity, a Boolean function measure related to circuit complexity. Given a circuit C over the standard basis {∨2, ∧2, ¬}, the energy complexity of C, denoted by EC(C), is the maximum number of its activated inner gates over all inputs. The energy complexity of a Boolean function f, denoted by EC(f), is the minimum of EC(C) over all circuits C computing f. Recently, K. Dinesh et al. [3] gave EC(f) an upper bound in terms of the decision tree complexity, EC(f) = O(D(f)3). They also showed that EC(f) ≤ 3n−1, where n is the input size. For the lower bound, they show that EC(f) ≥ 13 psens(f), where psens(f) is the positive sensitivity. They asked whether EC(f) can be lower bounded by a polynomial of D(f). We improve both the upper and lower bounds in this paper. For upper bounds, We show that EC(f) ≤ min{ 12 D(f)2 + O(D(f)), n + 2 D(f) − 2}. For the lower bound, we answer Dinesh et al.'s question by proving that EC(f) = Ω(pD(f)). For non-degenerated functions, we also give another lower bound EC(f) = Ω(log n) where n is the input size. These two lower bounds are incomparable to each other. Besides, we examine the energy complexity of OR functions and ADDRESS functions, which implies the tightness of our two lower bounds respectively. In addition, the former one answers another open question in [3] asking for non-trivial lower bound for energy complexity of OR functions. Copyright © 2018, The Authors. All rights reserved.

关键词： Decision trees