BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

140 related articles for article (PubMed ID: 36968494)

  • 1. Identification of autism spectrum disorder based on functional near-infrared spectroscopy using adaptive spatiotemporal graph convolution network.
    Zhang H; Xu L; Yu J; Li J; Wang J
    Front Neurosci; 2023; 17():1132231. PubMed ID: 36968494
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Classification of autism based on short-term spontaneous hemodynamic fluctuations using an adaptive graph neural network.
    Zhu Y; Xu L; Yu J
    J Neurosci Methods; 2023 Jul; 394():109901. PubMed ID: 37295750
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network.
    Xu L; Sun Z; Xie J; Yu J; Li J; Wang J
    Clin Neurophysiol; 2021 Feb; 132(2):457-468. PubMed ID: 33450566
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy.
    Xu L; Liu Y; Yu J; Li X; Yu X; Cheng H; Li J
    J Neurosci Methods; 2020 Feb; 331():108538. PubMed ID: 31794776
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction in Autism by Deep Learning Short-Time Spontaneous Hemodynamic Fluctuations.
    Xu L; Geng X; He X; Li J; Yu J
    Front Neurosci; 2019; 13():1120. PubMed ID: 31780879
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal.
    Xu L; Hua Q; Yu J; Li J
    Clin Neurophysiol; 2020 Jun; 131(6):1365-1374. PubMed ID: 32311592
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals.
    Wu T; Yin X; Xu L; Yu J
    J Neurosci Methods; 2024 May; ():110157. PubMed ID: 38705284
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis.
    Wen G; Cao P; Bao H; Yang W; Zheng T; Zaiane O
    Comput Biol Med; 2022 Mar; 142():105239. PubMed ID: 35066446
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network.
    Li C; Zhang T; Li J
    J Neurosci Methods; 2023 Jan; 383():109732. PubMed ID: 36349567
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Relationship between Short-Range and Homotopic Long-Range Resting State Functional Connectivity in Temporal Lobes in Autism Spectrum Disorder.
    Wu X; Lin F; Sun W; Zhang T; Sun H; Li J
    Brain Sci; 2021 Nov; 11(11):. PubMed ID: 34827466
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Regional-Asymmetric Adaptive Graph Convolutional Neural Network for Diagnosis of Autism in Children With Resting-State EEG.
    Hu W; Jiang G; Han J; Li X; Xie P
    IEEE Trans Neural Syst Rehabil Eng; 2024; 32():200-211. PubMed ID: 38145528
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network.
    Tigga NP; Garg S; Goyal N; Raj J; Das B
    Technol Health Care; 2024 Jun; ():. PubMed ID: 38943414
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder.
    Shao L; Fu C; Chen X
    BMC Bioinformatics; 2023 Sep; 24(1):363. PubMed ID: 37759189
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Attenuation of long-range temporal correlations of neuronal oscillations in young children with autism spectrum disorder.
    Jia H; Li Y; Yu D
    Neuroimage Clin; 2018; 20():424-432. PubMed ID: 30128281
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction.
    Jiang H; Cao P; Xu M; Yang J; Zaiane O
    Comput Biol Med; 2020 Dec; 127():104096. PubMed ID: 33166800
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Reduced interhemispheric functional connectivity of children with autism spectrum disorder: evidence from functional near infrared spectroscopy studies.
    Zhu H; Fan Y; Guo H; Huang D; He S
    Biomed Opt Express; 2014 Apr; 5(4):1262-74. PubMed ID: 24761305
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Altered complexity in resting-state fNIRS signal in autism: a multiscale entropy approach.
    Zhang T; Huang W; Wu X; Sun W; Lin F; Sun H; Li J
    Physiol Meas; 2021 Aug; 42(8):. PubMed ID: 34315139
    [No Abstract]   [Full Text] [Related]  

  • 18. The Development of Brain Network in Males with Autism Spectrum Disorders from Childhood to Adolescence: Evidence from fNIRS Study.
    Cao W; Zhu H; Li Y; Wang Y; Bai W; Lao U; Zhang Y; Ji Y; He S; Zou X
    Brain Sci; 2021 Jan; 11(1):. PubMed ID: 33477412
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Power spectrum of spontaneous cerebral homodynamic oscillation shows a distinct pattern in autism spectrum disorder.
    Cheng H; Yu J; Xu L; Li J
    Biomed Opt Express; 2019 Mar; 10(3):1383-1392. PubMed ID: 30891353
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Acquisition time for functional near-infrared spectroscopy resting-state functional connectivity in assessing autism.
    Wu X; Lin F; Zhang T; Sun H; Li J
    Neurophotonics; 2022 Oct; 9(4):045007. PubMed ID: 36466187
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.