Výsledky vyhledávání - "Unsupervised Machine Learning [MeSH]"

An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data

Autoři: Sönmez, Teresa Filshtein Harvey, Danielle J Beckett, Laurel A a další

Zdroj: Alzheimer's & Dementia. 21(2)

Témata: 32 Biomedical and Clinical Sciences (for-2020), 5202 Biological Psychology (for-2020), 3202 Clinical Sciences (for-2020), 3209 Neurosciences (for-2020), 52 Psychology (for-2020), Acquired Cognitive Impairment (rcdc), Alzheimer's Disease (rcdc), Neurodegenerative (rcdc), Brain Disorders (rcdc), Dementia (rcdc), Neurosciences (rcdc), Aging (rcdc), Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) (rcdc), 4.1 Discovery and preclinical testing of markers and technologies (hrcs-rac), 2.1 Biological and endogenous factors (hrcs-rac), 4.2 Evaluation of markers and technologies (hrcs-rac), Neurological (hrcs-hc), Humans (mesh), Alzheimer Disease (mesh), Biomarkers (mesh), Disease Progression (mesh), Cluster Analysis (mesh), Unsupervised Machine Learning (mesh), Neuroimaging (mesh), Female (mesh), Male (mesh), Aged (mesh), Brain (mesh), Amyloid beta-Peptides (mesh), Alzheimer's disease, amyloid cascade hypothesis, biomarker progression, cluster analysis, dementia, heterogeneity, K-means clustering, precision medicine, trajectories, unsupervised learning, Alzheimer's Disease Neuroimaging Initiative, Brain (mesh), Humans (mesh), Alzheimer Disease (mesh), Disease Progression (mesh), Cluster Analysis (mesh), Aged (mesh), Female (mesh), Male (mesh), Amyloid beta-Peptides (mesh), Neuroimaging (mesh), Biomarkers (mesh), Unsupervised Machine Learning (mesh), Alzheimer's disease, K‐means clustering, amyloid cascade hypothesis, biomarker progression, cluster analysis, dementia, heterogeneity, precision medicine, trajectories, unsupervised learning, Humans (mesh), Alzheimer Disease (mesh), Biomarkers (mesh), Disease Progression (mesh), Cluster Analysis (mesh), Unsupervised Machine Learning (mesh), Neuroimaging (mesh), Female (mesh), Male (mesh), Aged (mesh), Brain (mesh), Amyloid beta-Peptides (mesh), 1103 Clinical Sciences (for), 1109 Neurosciences (for), Geriatrics (science-metrix), 3202 Clinical sciences (for-2020), 3209 Neurosciences (for-2020), 5202 Biological psychology (for-2020)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/7qm9d8bw
https://escholarship.org/content/qt7qm9d8bw/qt7qm9d8bw.pdf

Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach

Autoři: Sun, Delin Adduru, Viraj R Phillips, Rachel D a další

Zdroj: Neuropsychopharmacology. 48(2)

Témata: 5202 Biological Psychology (for-2020), 32 Biomedical and Clinical Sciences (for-2020), 52 Psychology (for-2020), Alcoholism, Alcohol Use and Health (rcdc), Pediatric (rcdc), Biomedical Imaging (rcdc), Clinical Research (rcdc), Machine Learning and Artificial Intelligence (rcdc), Underage Drinking (rcdc), Behavioral and Social Science (rcdc), Substance Misuse (rcdc), Oral and gastrointestinal (hrcs-hc), Stroke (hrcs-hc), Cancer (hrcs-hc), 3 Good Health and Well Being (sdg), Adolescent (mesh), Humans (mesh), Aged (mesh), Underage Drinking (mesh), Unsupervised Machine Learning (mesh), Cerebral Cortical Thinning (mesh), Alcohol Drinking (mesh), Magnetic Resonance Imaging (mesh), Ethanol (mesh), Longitudinal Studies (mesh), Humans (mesh), Ethanol (mesh), Magnetic Resonance Imaging (mesh), Longitudinal Studies (mesh), Alcohol Drinking (mesh), Adolescent (mesh), Aged (mesh), Underage Drinking (mesh), Unsupervised Machine Learning (mesh), Cerebral Cortical Thinning (mesh), Adolescent (mesh), Humans (mesh), Aged (mesh), Underage Drinking (mesh), Unsupervised Machine Learning (mesh), Cerebral Cortical Thinning (mesh), Alcohol Drinking (mesh), Magnetic Resonance Imaging (mesh), Ethanol (mesh), Longitudinal Studies (mesh), 11 Medical and Health Sciences (for), 17 Psychology and Cognitive Sciences (for), Psychiatry (science-metrix), 3209 Neurosciences (for-2020), 5202 Biological psychology (for-2020)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/50q8974j
https://escholarship.org/content/qt50q8974j/qt50q8974j.pdf

Using hierarchical unsupervised learning to integrate and reduce multi-level and multi-paraspinal muscle MRI data in relation to low back pain

Autoři: Torres-Espin, Abel Keller, Anastasia Johnson, Gabriel TA a další

Zdroj: European Spine Journal. 31(8)

Témata: 4201 Allied Health and Rehabilitation Science (for-2020), 32 Biomedical and Clinical Sciences (for-2020), 3202 Clinical Sciences (for-2020), 42 Health Sciences (for-2020), Clinical Research (rcdc), Chronic Pain (rcdc), Neurosciences (rcdc), Biomedical Imaging (rcdc), Back Pain (rcdc), Pain Research (rcdc), 4.1 Discovery and preclinical testing of markers and technologies (hrcs-rac), Musculoskeletal (hrcs-hc), Neurological (hrcs-hc), Humans (mesh), Low Back Pain (mesh), Magnetic Resonance Imaging (mesh), Paraspinal Muscles (mesh), Unsupervised Machine Learning (mesh), Weightlessness (mesh), Lumbar spine, Paraspinal muscles, MRI, Low back pain, Integrative analysis, Multiple factor analysis, Hierarchical unsupervised learning, Humans (mesh), Low Back Pain (mesh), Magnetic Resonance Imaging (mesh), Weightlessness (mesh), Paraspinal Muscles (mesh), Unsupervised Machine Learning (mesh), Hierarchical unsupervised learning, Integrative analysis, Low back pain, Lumbar spine, MRI, Multiple factor analysis, Paraspinal muscles, Humans (mesh), Low Back Pain (mesh), Magnetic Resonance Imaging (mesh), Paraspinal Muscles (mesh), Unsupervised Machine Learning (mesh), Weightlessness (mesh), 0903 Biomedical Engineering (for), 1103 Clinical Sciences (for), Orthopedics (science-metrix), 3202 Clinical sciences (for-2020), 4201 Allied health and rehabilitation science (for-2020)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/4m91v0fn
https://escholarship.org/content/qt4m91v0fn/qt4m91v0fn.pdf

Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry

Autoři: Shi, Yanyan Strobl, Ralf Berner, Reinhard a další

Přispěvatelé: Shi, Yanyan Strobl, Ralf Berner, Reinhard a další

Zdroj: Respir Res
Respiratory Research, Vol 25, Iss 1, Pp 1-11 (2024)

Témata: Male, RC705-779, Adolescent, SARS-CoV-2, Research, COVID-19, Infant, Prognosis, Clustering, Hospitalization, Diseases of the respiratory system, Phenotype, Germany, Child, Preschool, Clinical phenotype, Machine learning, Hospitalization/statistics, Germany/epidemiology [MeSH], COVID-19/mortality [MeSH], Infant [MeSH], Male [MeSH], Phenotype [MeSH], COVID-19/epidemiology [MeSH], Child [MeSH], SARS-CoV-2 [MeSH], COVID-19/diagnosis [MeSH], Adolescent [MeSH], Female [MeSH], Humans [MeSH], Prospective Studies [MeSH], Unsupervised Machine Learning [MeSH], Prognosis [MeSH], Registries [MeSH], Child, Preschool [MeSH], Humans, Female, Registries, Prospective Studies, Child, Unsupervised Machine Learning

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39478555
https://doaj.org/article/ca421178e5794c8faca050c2c3dab88a
https://resolver.sub.uni-goettingen.de/purl?gro-2/146376
https://repository.publisso.de/resource/frl:6497464

Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment

Autoři: Julian Wenzel Nils Dreschke Esther Hanssen a další

Zdroj: Eur Arch Psychiatry Clin Neurosci

Témata: Male, Adult, Original Paper, Ecological Momentary Assessment, Middle Aged, Female [MeSH], Adult [MeSH], Dynamic time warping, Ecological Momentary Assessment [MeSH], Humans [MeSH], Unsupervised Machine Learning [MeSH], Longitudinal Studies [MeSH], Middle Aged [MeSH], Psychosis, Clustering, Cluster Analysis [MeSH], Unsupervised machine learning, EMA, Male [MeSH], Psychotic Disorders/diagnosis [MeSH], Young Adult [MeSH], Psychotic Disorders/physiopathology [MeSH], 3. Good health, Young Adult, Psychotic Disorders, Humans, Cluster Analysis, Female, Longitudinal Studies, Unsupervised Machine Learning

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/37715784
https://repository.publisso.de/resource/frl:6507780

Unsupervised word embeddings capture latent knowledge from materials science literature

Autoři: Tshitoyan, Vahe Dagdelen, John Weston, Leigh a další

Zdroj: Nature. 571(7763)

Témata: 46 Information and Computing Sciences (for-2020), 4611 Machine Learning (for-2020), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Generic health relevance (hrcs-hc), Data Mining (mesh), Electric Conductivity (mesh), Electrodes (mesh), Iron (mesh), Knowledge (mesh), Lithium (mesh), Magnetics (mesh), Materials Science (mesh), Natural Language Processing (mesh), Reproducibility of Results (mesh), Research (mesh), Research Report (mesh), Semantics (mesh), Temperature (mesh), Terminology as Topic (mesh), Unsupervised Machine Learning (mesh), Lithium (mesh), Iron (mesh), Reproducibility of Results (mesh), Electrodes (mesh), Temperature (mesh), Electric Conductivity (mesh), Magnetics (mesh), Knowledge (mesh), Research (mesh), Semantics (mesh), Natural Language Processing (mesh), Terminology as Topic (mesh), Data Mining (mesh), Research Report (mesh), Unsupervised Machine Learning (mesh), Materials Science (mesh), Data Mining (mesh), Electric Conductivity (mesh), Electrodes (mesh), Iron (mesh), Knowledge (mesh), Lithium (mesh), Magnetics (mesh), Materials Science (mesh), Natural Language Processing (mesh), Reproducibility of Results (mesh), Research (mesh), Research Report (mesh), Semantics (mesh), Temperature (mesh), Terminology as Topic (mesh), Unsupervised Machine Learning (mesh), General Science & Technology (science-metrix)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/082091b4
https://escholarship.org/content/qt082091b4/qt082091b4.pdf

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics

Autoři: Hench, Jürgen Hultschig, Claus Brugger, Jon a další

Zdroj: Acta Neuropathol Commun
Acta Neuropathologica Communications, Vol 12, Iss 1, Pp 1-16 (2024)
Acta neuropathologica communications, vol. 12, no. 1, pp. 51

Témata: Epigenomics, Artificial intelligence, Nanopore sequencing, Microarray, Methylation, Neoplasms, Humans, Unsupervised Machine Learning, Cloud Computing, Neoplasms/diagnosis, Neoplasms/genetics, DNA Methylation, Copy number profiling, Cryptocurrency miner, Digital pathology, Dimension reduction, Edge computing, Epigenetics, Intraoperative, Methylation sequencing, Methylome, Oncology, Same-day classification, SoC, Tumour, UMAP, Unsupervised machine learning, gpGPU, RC346-429, Cancer genetics, Methodology Article, 3. Good health, Medical genetics (excl. cancer genetics), Neurology. Diseases of the nervous system, Epigenomics [MeSH], Humans [MeSH], Unsupervised Machine Learning [MeSH], Neoplasms/diagnosis [MeSH], Neoplasms/genetics [MeSH], Cloud Computing [MeSH], DNA Methylation [MeSH]

Popis souboru: application/pdf

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38576030
https://doaj.org/article/0728b5481b5942bf8a38046de711832e
https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-024-01759-2
https://hdl.handle.net/11588/992809
http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_AFEC867AF2600
https://serval.unil.ch/resource/serval:BIB_AFEC867AF260.P001/REF.pdf
https://serval.unil.ch/notice/serval:BIB_AFEC867AF260
https://repository.publisso.de/resource/frl:6498770
https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-024-01759-2
https://doi.org/10.1186/s40478-024-01759-2
https://hdl.handle.net/11588/992809

PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution

Autoři: Gordon, Sean P Levy, Joshua J Vogel, John P

Zdroj: BMC Genomics. 20(1)

Témata: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 3105 Genetics (for-2020), Genetics (rcdc), Human Genome (rcdc), DNA, Plant (mesh), Evolution, Molecular (mesh), Genome, Plant (mesh), Genomics (mesh), Phylogeny (mesh), Polyploidy (mesh), Repetitive Sequences, Nucleic Acid (mesh), Nicotiana (mesh), Triticum (mesh), Unsupervised Machine Learning (mesh), Allopolyploid, K-mer, Transposon, Binning, Evolution, Repetitive DNA, Subgenome, Wheat, Tobacco, Triticum (mesh), DNA, Plant (mesh), Genomics (mesh), Evolution, Molecular (mesh), Phylogeny (mesh), Repetitive Sequences, Nucleic Acid (mesh), Polyploidy (mesh), Genome, Plant (mesh), Unsupervised Machine Learning (mesh), Nicotiana (mesh), Allopolyploid, Binning, Evolution, K-mer, Repetitive DNA, Subgenome, Tobacco, Transposon, Wheat, DNA, Plant (mesh), Evolution, Molecular (mesh), Genome, Plant (mesh), Genomics (mesh), Phylogeny (mesh), Polyploidy (mesh), Repetitive Sequences, Nucleic Acid (mesh), Nicotiana (mesh), Triticum (mesh), Unsupervised Machine Learning (mesh), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), 11 Medical and Health Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 32 Biomedical and clinical sciences (for-2020)

Přístupová URL adresa: https://escholarship.org/uc/item/4k19w23q

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression

Autoři: Christopher, Mark Belghith, Akram Weinreb, Robert N a další

Zdroj: Investigative Ophthalmology & Visual Science. 59(7)

Témata: 32 Biomedical and Clinical Sciences (for-2020), 3212 Ophthalmology and Optometry (for-2020), Neurodegenerative (rcdc), Biomedical Imaging (rcdc), Bioengineering (rcdc), Clinical Research (rcdc), Neurosciences (rcdc), Machine Learning and Artificial Intelligence (rcdc), Eye Disease and Disorders of Vision (rcdc), Aging (rcdc), 4.2 Evaluation of markers and technologies (hrcs-rac), 4.1 Discovery and preclinical testing of markers and technologies (hrcs-rac), Eye (hrcs-hc), Adult (mesh), Aged (mesh), Disease Progression (mesh), Female (mesh), Glaucoma, Open-Angle (mesh), Humans (mesh), Intraocular Pressure (mesh), Male (mesh), Middle Aged (mesh), Nerve Fibers (mesh), Optic Disk (mesh), Optic Nerve Diseases (mesh), Principal Component Analysis (mesh), Retinal Ganglion Cells (mesh), Retrospective Studies (mesh), Tomography, Optical Coherence (mesh), Tonometry, Ocular (mesh), Unsupervised Machine Learning (mesh), Visual Field Tests (mesh), machine learning, glaucoma progression, retinal nerve fiber layer, Nerve Fibers (mesh), Retinal Ganglion Cells (mesh), Optic Disk (mesh), Humans (mesh), Optic Nerve Diseases (mesh), Glaucoma, Open-Angle (mesh), Disease Progression (mesh), Tomography, Optical Coherence (mesh), Tonometry, Ocular (mesh), Retrospective Studies (mesh), Intraocular Pressure (mesh), Principal Component Analysis (mesh), Adult (mesh), Aged (mesh), Middle Aged (mesh), Female (mesh), Male (mesh), Visual Field Tests (mesh), Unsupervised Machine Learning (mesh), Adult (mesh), Aged (mesh), Disease Progression (mesh), Female (mesh), Glaucoma, Open-Angle (mesh), Humans (mesh), Intraocular Pressure (mesh), Male (mesh), Middle Aged (mesh), Nerve Fibers (mesh), Optic Disk (mesh), Optic Nerve Diseases (mesh), Principal Component Analysis (mesh), Retinal Ganglion Cells (mesh), Retrospective Studies (mesh), Tomography, Optical Coherence (mesh), Tonometry, Ocular (mesh), Unsupervised Machine Learning (mesh), Visual Field Tests (mesh), 06 Biological Sciences (for), 11 Medical and Health Sciences (for), Ophthalmology & Optometry (science-metrix), 3212 Ophthalmology and optometry (for-2020)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/3666b0bv
https://escholarship.org/content/qt3666b0bv/qt3666b0bv.pdf

Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Autoři: Katharina Brosch Stefanie Heilmann-Heimbach Stephanie H. Witt a další

Zdroj: Neuropsychopharmacology
Neuropsychopharmacology 46(11), 1895-1905 (2021). doi:10.1038/s41386-021-01051-0

Témata: 03 medical and health sciences, Bipolar Disorder, 0302 clinical medicine, Psychotic Disorders, Mental Disorders, Schizophrenia, Humans, Bipolar Disorder/diagnosis [MeSH], Mental Disorders/genetics [MeSH], Humans [MeSH], Unsupervised Machine Learning [MeSH], Schizophrenia [MeSH], Mental Disorders/diagnosis [MeSH], Translational research, Bipolar Disorder/genetics [MeSH], Article, Depression, Genetics, Psychology, Psychiatric disorders, Psychotic Disorders [MeSH], ddc, Unsupervised Machine Learning, 3. Good health

Popis souboru: application/pdf

Přístupová URL adresa: https://europepmc.org/articles/pmc8429672?pdf=render
https://www.nature.com/articles/s41386-021-01051-0.pdf
https://pubmed.ncbi.nlm.nih.gov/34127797
https://www.medrxiv.org/content/medrxiv/early/2021/02/08/2021.02.04.21251083.full.pdf
https://www.medrxiv.org/content/10.1101/2021.02.04.21251083v1
https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3330367
https://www.nature.com/articles/s41386-021-01051-0.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429672
https://europepmc.org/article/PPR/PPR279401
https://www.nature.com/articles/s41386-021-01051-0
https://pubmed.ncbi.nlm.nih.gov/34127797/
https://juser.fz-juelich.de/record/903472
https://repository.publisso.de/resource/frl:6443710
https://epub.ub.uni-muenchen.de/117494/
https://mediatum.ub.tum.de/1634520

Defining Trends in Global Gene Expression in Arabian Horses with Cerebellar Abiotrophy

Autoři: Scott, EY Penedo, MCT Murray, JD a další

Zdroj: The Cerebellum. 16(2)

Témata: 32 Biomedical and Clinical Sciences (for-2020), 5202 Biological Psychology (for-2020), 5204 Cognitive and Computational Psychology (for-2020), 3209 Neurosciences (for-2020), 52 Psychology (for-2020), Brain Disorders (rcdc), Neurodegenerative (rcdc), Genetics (rcdc), Neurosciences (rcdc), Aging (rcdc), Human Genome (rcdc), Rare Diseases (rcdc), 2.1 Biological and endogenous factors (hrcs-rac), Neurological (hrcs-hc), Animals (mesh), Cerebellum (mesh), Cluster Analysis (mesh), DNA Glycosylases (mesh), Female (mesh), Gene Expression (mesh), Heredodegenerative Disorders, Nervous System (mesh), Horse Diseases (mesh), Horses (mesh), Male (mesh), Polymerase Chain Reaction (mesh), Polymorphism, Single Nucleotide (mesh), Transcriptome (mesh), Unsupervised Machine Learning (mesh), Cerebellar abiotrophy, Horse, Calcium, Microglial activation, RNA-seq, Cerebellum (mesh), Animals (mesh), Horses (mesh), Heredodegenerative Disorders, Nervous System (mesh), Horse Diseases (mesh), DNA Glycosylases (mesh), Cluster Analysis (mesh), Polymerase Chain Reaction (mesh), Gene Expression (mesh), Polymorphism, Single Nucleotide (mesh), Female (mesh), Male (mesh), Transcriptome (mesh), Unsupervised Machine Learning (mesh), Calcium, Cerebellar abiotrophy, Horse, Microglial activation, RNA-seq, Animals (mesh), Cerebellum (mesh), Cluster Analysis (mesh), DNA Glycosylases (mesh), Female (mesh), Gene Expression (mesh), Heredodegenerative Disorders, Nervous System (mesh), Horse Diseases (mesh), Horses (mesh), Male (mesh), Polymerase Chain Reaction (mesh), Polymorphism, Single Nucleotide (mesh), Transcriptome (mesh), Unsupervised Machine Learning (mesh), 1103 Clinical Sciences (for), 1109 Neurosciences (for), 1702 Cognitive Sciences (for), Neurology & Neurosurgery (science-metrix), 3209 Neurosciences (for-2020), 5202 Biological psychology (for-2020), 5204 Cognitive and computational psychology (for-2020)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/2tk3r44q
https://escholarship.org/content/qt2tk3r44q/qt2tk3r44q.pdf

When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections

Autoři: Zhong, Cheng Han, Ju Borowsky, Alexander a další

Zdroj: Medical Image Analysis. 35

Témata: 32 Biomedical and Clinical Sciences (for-2020), 40 Engineering (for-2020), Brain Disorders (rcdc), Brain Cancer (rcdc), Cancer (rcdc), Rare Diseases (rcdc), Color (mesh), Histological Techniques (mesh), Humans (mesh), Pattern Recognition, Automated (mesh), Unsupervised Machine Learning (mesh), Computational histopathology, Classification, Unsupervised feature learning, Sparse feature encoder, Humans (mesh), Histological Techniques (mesh), Color (mesh), Pattern Recognition, Automated (mesh), Unsupervised Machine Learning (mesh), Classification, Computational histopathology, Sparse feature encoder, Unsupervised feature learning, Color (mesh), Histological Techniques (mesh), Humans (mesh), Pattern Recognition, Automated (mesh), Unsupervised Machine Learning (mesh), 09 Engineering (for), 11 Medical and Health Sciences (for), Nuclear Medicine & Medical Imaging (science-metrix), 32 Biomedical and clinical sciences (for-2020), 40 Engineering (for-2020)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/9kf5g23x
https://escholarship.org/content/qt9kf5g23x/qt9kf5g23x.pdf

Modeling brain dynamic state changes with adaptive mixture independent component analysis

Autoři: Hsu, Sheng-Hsiou Pion-Tonachini, Luca Palmer, Jason a další

Zdroj: NeuroImage, vol 183

Témata: Data Interpretation, Adaptive mixture ICA (AMICA), Medical Physiology, 02 engineering and technology, 3208 Medical Physiology (for-2020), 11 Medical and Health Sciences (for), Medical and Health Sciences, Wakefulness (mesh), Clinical Research (rcdc), Computer-Assisted, 0302 clinical medicine, Theoretical, Models, 0202 electrical engineering, electronic engineering, information engineering, Adaptive mixture ICA, Neurology & Neurosurgery (science-metrix), 32 Biomedical and Clinical Sciences (for-2020), Neurosciences (rcdc), Cerebral Cortex, Humans (mesh), Networking and Information Technology R&D (NITRD) (rcdc), Bioengineering (rcdc), Brain states, Electroencephalography, Signal Processing, Computer-Assisted, Statistical, Theoretical (mesh), Mental Health, Sleep Stages (mesh), Networking and Information Technology R&D (NITRD), Data Interpretation, Statistical, Neurological, Sleep Stages, Sleep Research, Behavioral and Social Science (rcdc), Statistical (mesh), Mental Health (rcdc), Adult, Electroencephalography (mesh), 1.1 Normal biological development and functioning, Independent component analysis (ICA), Bioengineering, Independent component analysis, Basic Behavioral and Social Science, Unsupervised learning, 03 medical and health sciences, Clinical Research, 42 Health sciences (for-2020), Behavioral and Social Science, Humans, Sleep Research (rcdc), 17 Psychology and Cognitive Sciences (for), Unsupervised Machine Learning (mesh), Electroencephalography (EEG), Wakefulness, 1.1 Normal biological development and functioning (hrcs-rac), Neurology & Neurosurgery, Biomedical and Clinical Sciences, Drowsiness detection, Neurological (hrcs-hc), Psychology and Cognitive Sciences, Non-stationarity, Neurosciences, Health sciences, Models, Theoretical, Computer-Assisted (mesh), Sleep staging, 32 Biomedical and clinical sciences (for-2020), Signal Processing, Adult (mesh), Cerebral Cortex (mesh), Basic Behavioral and Social Science (rcdc), Unsupervised Machine Learning

Popis souboru: application/pdf

Přístupová URL adresa: https://europepmc.org/articles/pmc6205696?pdf=render
https://pubmed.ncbi.nlm.nih.gov/30086409
https://www.sciencedirect.com/science/article/pii/S1053811918306888
https://pubmed.ncbi.nlm.nih.gov/30086409/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6205696
https://dblp.uni-trier.de/db/journals/neuroimage/neuroimage183.html#HsuPPMMJ18
https://escholarship.org/content/qt34s9q3s5/qt34s9q3s5.pdf
https://escholarship.org/uc/item/34s9q3s5

Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Autoři: Pelin, Helena Ising, Marcus Stein, Frederike a další

Zdroj: http://lobid.org/resources/99370679077206441#!, 46(11):1895-1905.

Témata: Bipolar Disorder/diagnosis [MeSH], Mental Disorders/genetics [MeSH], Humans [MeSH], Unsupervised Machine Learning [MeSH], Schizophrenia [MeSH], Mental Disorders/diagnosis [MeSH], Translational research, Bipolar Disorder/genetics [MeSH], Article, Depression, Genetics, Psychology, Psychiatric disorders, Psychotic Disorders [MeSH]

Relation: https://repository.publisso.de/resource/frl:6443710; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429672/

Dostupnost: https://repository.publisso.de/resource/frl:6443710
https://doi.org/10.1038/s41386-021-01051-0
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429672/

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG

Autoři: Hsu, Sheng-Hsiou Lin, Yayu Onton, Julie a další

Zdroj: NeuroImage, Vol 249, Iss, Pp 118873-(2022)
NeuroImage, vol 249

Témata: Adaptive mixture ICA (AMICA), 1.2 Psychological and socioeconomic processes, Emotions, 11 Medical and Health Sciences (for), Medical and Health Sciences, Clinical Research (rcdc), 0302 clinical medicine, Adaptive mixture ICA, Neurology & Neurosurgery (science-metrix), 32 Biomedical and Clinical Sciences (for-2020), Neurosciences (rcdc), 42 Health Sciences (for-2020), 1.2 Psychological and socioeconomic processes (hrcs-rac), Cerebral Cortex, Humans (mesh), 05 social sciences, Brain states, Affective computing, Electroencephalography, 16. Peace & justice, Emotions (mesh), Mental Health, Imagination, Imagination (mesh), Mind and Body (rcdc), Behavioral and Social Science (rcdc), Source localization, RC321-571, Mental Health (rcdc), Adult, Electroencephalography (mesh), 1.1 Normal biological development and functioning, Independent component analysis (ICA), Neurosciences. Biological psychiatry. Neuropsychiatry, Independent component analysis, Basic Behavioral and Social Science, Unsupervised learning, 03 medical and health sciences, Clinical Research, 42 Health sciences (for-2020), Health Sciences, Behavioral and Social Science, Humans, 0501 psychology and cognitive sciences, 17 Psychology and Cognitive Sciences (for), Unsupervised Machine Learning (mesh), Electroencephalography (EEG), Functional Neuroimaging (mesh), 1.1 Normal biological development and functioning (hrcs-rac), Emotion, Neurology & Neurosurgery, Biomedical and Clinical Sciences, Functional Neuroimaging, Psychology and Cognitive Sciences, Non-stationarity, Neurosciences, 32 Biomedical and clinical sciences (for-2020), Adult (mesh), Cerebral Cortex (mesh), Basic Behavioral and Social Science (rcdc), Mind and Body, Unsupervised Machine Learning

Popis souboru: application/pdf

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/34998969
https://doaj.org/article/c37c9ed7e2604723ae45132ffe159eee
https://escholarship.org/uc/item/5q48611f
https://escholarship.org/content/qt5q48611f/qt5q48611f.pdf