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Author Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

Nature genetics 2023年第6期55卷 1078页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Samsung Genome Institute Samsung Medical Center Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Samsung Advanced Institute for Health Sciences and Technology Sungkyunkwan University School of Medicine Seoul South Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Department of Haematology University of Cambridge Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson.org. Quantitative and Comp

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Core Collapse Supernova Gravitational Wave Emission for Progenitors of 9.6, 15, and 25 M☉

arXiv

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arXiv 2022年

作者： Mezzacappa, Anthony Marronetti, Pedro Landfield, Ryan E. Lentz, Eric J. Hix, W. Raphael Harris, J. Austin Bruenn, Stephen W. Blondin, John M. Messer, O.E. Bronson Casanova, Jordi Kronzer, Luke L. Department of Physics and Astronomy University of Tennessee 1408 Circle Drive KnoxvilleTN37996-1200 United States Physics Division National Science Foundation AlexandriaVA22314 United States National Center for Computational Sciences Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831-6164 United States Physics Division Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831-6354 United States Joint Institute for Nuclear Physics and its Applications Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831-6374 United States Department of Physics Florida Atlantic University 777 Glades Road Boca RatonFL33431-0991 United States Department of Physics North Carolina State University RaleighNC27695-8202 United States Physics Program Community College of Denver P.O. Box 173363 DenverCO80217-3363 United States Department of Aerospace Engineering and Mechanics University of Alabama Box 870280 TuscaloosaAL35487-0280 United States

We present gravitational wave emission predictions based on three core collapse supernova simulations corresponding to three different progenitor masses. The masses span a large range, between 9.6 and 25 M☉ , are all initially non-rotating, and are of two metallicities: zero and Solar. We compute both the temporal evolution of the gravitational wave strains for both the plus and the cross polarizations, as well as their spectral decomposition and characteristic strains. The temporal evolution of our zero metallicity, 9.6 M☉ progenitor model is distinct from the temporal evolution of our Solar metallicity, 15 M☉ progenitor model and our zero metallicity, 25 M☉ progenitor model. In the former case, the high-frequency gravitational wave emission is largely confined to a brief time period ∼75 ms after bounce, whereas in the latter two cases, high-frequency emission does not commence until ∼125 ms after bounce or later. Nonetheless, the physical origin of the high-frequency emission in all three cases corresponds to convection in the proto-neutron star of both Schwarzschild and Ledoux type, and convective overshoot. The low-frequency emission in all three models exhibits very similar behavior. At frequencies below ∼250 Hz, gravitational waves are emitted by neutrino-driven convection and the SASI. This emission extends throughout the simulations when a gain region is present. In all three models, explosion is observed, at ∼125, ∼500, and ∼250 ms after bounce in the 9.6, 15, and 25 M☉ progenitor models, respectively. At these times, the low-frequency gravitational wave emission is joined by very low frequency emission, below ∼10 Hz. These very low frequency episodes are the result of explosion and begin at the above designated explosion times in each of our models. Our characteristic strains tell us that in principle all three gravitational wave signals would be detectable by current-generation detectors for a supernova at a distance of 10 kpc. However, our 9.6 M☉ progeni

关键词： Strain

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DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

arXiv

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arXiv 2019年

作者： Wang, Haidi Chen, Weijie Zeng, Jinzhe Zhang, Linfeng Wang, Han Zhang, Yuzhi Weinan, E. Yuanpei College of Peking University Beijing100871 China School of Electronic Science and Applied Physics Hefei University of Technology Hefei230601 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Beijing Institute of Big Data Research Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Beijing Institute of Big Data Research Beijing100871 China

In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed"on-the-fly" learning procedure [Phys. Rev. Materials 3, 023804] and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN. Copyright © 2019, The Authors. All rights reserved.

关键词： Potential energy

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Integrating yeast chemical genomics and mammalian cell pathway analysis

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中国药理学报（英文版） 2019年第9期40卷 1245-1255页

作者： Fu-lai Zhou Sheena C Li Yue Zhu Wan-jing Guo Li-jun Shao Justin Nelson Scott Simpkins De-hua Yang Qing Liu Yoko Yashiroda Jin-biao Xu Yao-yue Fan Jian-min Yue Minoru Yoshida Tian Xia Chad L Myers Charles Boone Ming-wei Wang The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia MedicaChinese Academy of Sciences(CAS)Shanghai 201203China University of Chinese Academy of Sciences Beijing 100049China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Bioinformatics and Computational Biology Program University of Minnesota-Twin CitiesMinneapolisMinnesota 55455USA The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia MedicaChinese Academy of Sciences(CAS)Shanghai 201203China The State Key Laboratory of Drug Research Shanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Department of Biology The University of TokyoBunkyo-kuTokyo 1138657Japan Collaborative Research for Innovative Microbiology The University of TokyoBunkyo-kuTokyo 1138657Japan Department of Electronics and Information Engineering Huazhong University of Science and TechnologyWuhan 430074China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Donnelly Centre and Department of Molecular Genetics University of TorontoOntario M5S 3E1Canada

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces ***,we use yeast as a surrogate system for studying compounds that are active against metazoan ***-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target *** discover compounds that have the potential to function like therapeutic agents with known targets,we also analyzed a reference library of approved *** uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells,and new modulators with specific modes of action were *** analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

关键词： yeast chemical genomics tubulin cytoskeleton assembly autophagy Wnt/β-catenin signaling pathway

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Void distributions reveal structural link between jammed packings and protein cores

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Physical Review E 2019年第2期99卷 022416-022416页

作者： John D. Treado Zhe Mei Lynne Regan Corey S. O'Hern Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical & Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Chemistry Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction ϕ≈0.56, which is the same as dense, random packing of amino-acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino-acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. We measure the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation. We find that the cluster size statistics are similar for void percolation in packings of amino-acid-shaped particles and randomly placed spheres, but different from that for void percolation in jammed sphere packings. We propose that the connected void regions are a defining structural feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino-acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations.

关键词： Jamming Protein structure Packing & jamming problems Proteins Crystal structures Finite-size scaling Molecular dynamics Percolation theory

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SUBCELLULAR TO TISSUE SCALE MODELING OF ISCHEMIA AND SPREADING DEPOLARIZATION

引用

IBRO Neuroscience Reports 2023年 15卷 S792-S792页

作者： Adam Newton Craig Kelley Amy Guo Joy Wang Sydney Zink Marcello Distasio Robert A Mcdougal William Lytton SUNY Downstate Health Sciences University Physiology And Pharamcology New York United States of America SUNY Downstate Health Sciences University Program In Biomedical Engineering New York United States of America Yale School of Public Health Health Informatics Program NEW HAVEN United States of America Yale School of Medicine Department Of Pathology NEW HAVEN United States of America Yale University Department Of Biostatistics NEW HAVEN United States of America Yale University Center For Medical Informatics NEW HAVEN United States of America Yale University Program In Computational Biology And Bioinformatic NEW HAVEN United States of America Yale University Wu Tsai Institute NEW HAVEN United States of America SUNY Downstate Health Sciences University Department Of Neurology New York United States of America Kings County Hospital Center Department Of Neurology New York United States of America SUNY Downstate Health Sciences University The Robert F. Furchgott Center Neural And Behavioral Science New York United States of America

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Ten quick tips for deep learning in biology

arXiv

引用

arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Current md forcefields fail to capture key features of protein structure and fluctuations: a case study of cyclophilin a and t4 lysozyme

arXiv

引用

arXiv 2020年

作者： Mei, Zhe Grigas, Alex T. Treado, John D. Corres, Gabriel Melendez Vuorte, Maisa Sammalkorpi, Maria Regan, Lynne Levine, Zachary A. O’Hern, Corey S. Department of Chemistry Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Biology UPR Humacao Puerto RicoHumacao00792 Puerto Rico Department of Chemistry and Materials Science School of Chemical Engineering Aalto University Aalto Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Department of Pathology Yale School of Medicine New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores. © 2020, CC BY.

关键词： Molecular dynamics

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From mechanism to medicine: The progress and potential of epigenetics in osteoarthritis

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Osteoarthritis and Cartilage Open 2025年第3期7卷 100621-100621页

作者： Roberts, Jack B. Rockel, Jason S. Mulders, Rick Capellini, Terence D. Appleton, C. Thomas Phanstiel, Douglas H. Lories, Rik Geurts, Jeroen Ali, Shabana Amanda Bhutani, Nidhi Stone, Laura Cruz-Almeida, Yenisel Jurisica, Igor Boer, Cindy G. Ramos, Yolande F.M. Rice, Sarah J. Kapoor, Mohit Biosciences Institute Newcastle University Newcastle upon Tyne United Kingdom Division of Orthopedics Osteoarthritis Research Program Schroeder Arthritis Institute University Health Network and Krembil Research Institute University Health Network Toronto ON Canada Department of Medical Biophysics and Computer Science University of Toronto Toronto ON Canada Department of Biomedical Data Sciences Section Molecular Epidemiology Leiden University Medical Center Leiden Netherlands Department of Human Evolutionary Biology Harvard University Cambridge MA United States Bone and Joint Institute and Department of Medicine Schulich School of Medicine and Dentistry Western University London Canada Curriculum in Bioinformatics and Computational Biology University of North Carolina Chapel Hill NC United States Laboratory of Tissue Homeostasis and Disease Skeletal Biology and Engineering Research Centre Department of Development and Regeneration KU Leuven Leuven Belgium Division of Rheumatology University Hospitals Leuven Leuven Belgium Rheumatology Department of Musculoskeletal Medicine Lausanne University Hospital and University of Lausanne (CHUV-UNIL) Lausanne Switzerland Bone and Joint Center Henry Ford Health + Michigan State University Health Sciences Detroit MI United States Department of Orthopaedic Surgery Stanford School of Medicine Stanford CA United States Department of Anesthesiology School of Medicine University of Minnesota Minneapolis MN United States Pain Research and Intervention Center of Excellence (PRICE) at the University of Florida Gainesville FL United States Department of Internal Medicine Genomics Medicine Center Erasmus MC University Medical Center Rotterdam Netherlands Department of Laboratory Medicine and Pathobiology University of Toronto Toronto ON Canada Department of Surgery University of Toronto Toronto ON Canada

Objective: Osteoarthritis (OA) is a chronic, degenerative disease of the articular joints. The disease presents an enormous clinical and economic burden globally, due in part to the lack of disease modifying therapies. For over a decade, OA researchers have been working to determine epigenetic mechanisms underlying the disease to better understand pathology, identify biomarkers of progression, and pinpoint novel targets for therapeutic intervention. Design: This article presents a summary of the 3rd International Workshop on the Epigenetics of Osteoarthritis held in Toronto, Ontario, Canada, in September 2024. The purpose of this meeting was to gather the international community to discuss the status of OA epigenetic research and share expertise on innovative techniques for future. Results: Since the two previous meetings, there has been increasing adoption of advanced single-cell and spatial sequencing technologies and bioinfomatic analyses. Furthermore, investigations of multiple joint tissues has highlighted the shifting paradigm from OA as a cartilage centric disease to the consideration of all joint tissues. Conclusions: The workshop provided a unique opportunity for early-career researchers to expand their network, and for all participants to discuss new or improved approaches to advance the field, including international consortia and data sharing. The highlights and outcomes from this OA epigenetics workshop are described in this report. © 2025 The Authors

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Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

arXiv

引用

arXiv 2020年

作者： Ezaki, Takahiro Himeno, Yu Watanabe, Takamitsu Masuda, Naoki Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan PRESTO JST 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Aeronautics and Astronautics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Laboratory for Cognition Circuit Dynamics RIKEN Centre for Brain Science Saitama351-0198 Japan International Research Center for Neurointelligence The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants, and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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