357 related articles for article (PubMed ID: 32592530)
21. From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies.
Chen Z; Jamadar SD; Li S; Sforazzini F; Baran J; Ferris N; Shah NJ; Egan GF
Hum Brain Mapp; 2018 Dec; 39(12):5126-5144. PubMed ID: 30076750
[TBL] [Abstract][Full Text] [Related]
22. A whitening approach to probabilistic canonical correlation analysis for omics data integration.
Jendoubi T; Strimmer K
BMC Bioinformatics; 2019 Jan; 20(1):15. PubMed ID: 30626338
[TBL] [Abstract][Full Text] [Related]
23. A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods.
Chen Z; Liang K; Ding SX; Yang C; Peng T; Yuan X
IEEE Trans Neural Netw Learn Syst; 2022 Nov; 33(11):6158-6172. PubMed ID: 33886482
[TBL] [Abstract][Full Text] [Related]
24. Rotation in Correspondence Analysis from the Canonical Correlation Perspective.
Makino N
Psychometrika; 2022 Sep; 87(3):1045-1063. PubMed ID: 35031941
[TBL] [Abstract][Full Text] [Related]
25. [Current status and future perspectives of near-infrared spectroscopy: clinical applications and neurocognitive findings].
Minagawa-Kawai Y
Brain Nerve; 2012 Sep; 64(9):1023-32. PubMed ID: 22941840
[TBL] [Abstract][Full Text] [Related]
26. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis.
Hossain MS; Chowdhury MEH; Reaz MBI; Ali SHM; Bakar AAA; Kiranyaz S; Khandakar A; Alhatou M; Habib R; Hossain MM
Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590859
[TBL] [Abstract][Full Text] [Related]
27. An automated machine learning approach to predict brain age from cortical anatomical measures.
Dafflon J; Pinaya WHL; Turkheimer F; Cole JH; Leech R; Harris MA; Cox SR; Whalley HC; McIntosh AM; Hellyer PJ
Hum Brain Mapp; 2020 Sep; 41(13):3555-3566. PubMed ID: 32415917
[TBL] [Abstract][Full Text] [Related]
28. Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics.
Kim M; Min EJ; Liu K; Yan J; Saykin AJ; Moore JH; Long Q; Shen L
Med Image Anal; 2022 Feb; 76():102297. PubMed ID: 34871929
[TBL] [Abstract][Full Text] [Related]
29. Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.
Hao X; Li C; Yan J; Yao X; Risacher SL; Saykin AJ; Shen L; Zhang D;
Bioinformatics; 2017 Jul; 33(14):i341-i349. PubMed ID: 28881979
[TBL] [Abstract][Full Text] [Related]
30. A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans.
Yu Z; Han X; Xu W; Zhang J; Marr C; Shen D; Peng T; Zhang XY; Feng J
Elife; 2022 Dec; 11():. PubMed ID: 36546674
[TBL] [Abstract][Full Text] [Related]
31. Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis.
Sun L; Ji S; Ye J
IEEE Trans Pattern Anal Mach Intell; 2011 Jan; 33(1):194-200. PubMed ID: 20733223
[TBL] [Abstract][Full Text] [Related]
32. SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration.
Moon S; Hwang J; Lee H
J Comput Biol; 2022 Aug; 29(8):892-907. PubMed ID: 35951002
[TBL] [Abstract][Full Text] [Related]
33. A hierarchical independent component analysis model for longitudinal neuroimaging studies.
Wang Y; Guo Y
Neuroimage; 2019 Apr; 189():380-400. PubMed ID: 30639837
[TBL] [Abstract][Full Text] [Related]
34. Spatio-Spectral CCA (SS-CCA): A novel approach for frequency recognition in SSVEP-based BCI.
Norizadeh Cherloo M; Kashefi Amiri H; Daliri MR
J Neurosci Methods; 2022 Apr; 371():109499. PubMed ID: 35151668
[TBL] [Abstract][Full Text] [Related]
35. Performing Sparse Regularization and Dimension Reduction Simultaneously in Multimodal Data Fusion.
Yang Z; Zhuang X; Bird C; Sreenivasan K; Mishra V; Banks S; Cordes D;
Front Neurosci; 2019; 13():642. PubMed ID: 31333396
[TBL] [Abstract][Full Text] [Related]
36. A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.
Jiao Y; Zhang Y; Wang Y; Wang B; Jin J; Wang X
Int J Neural Syst; 2018 May; 28(4):1750039. PubMed ID: 28982285
[TBL] [Abstract][Full Text] [Related]
37. A learning algorithm for adaptive canonical correlation analysis of several data sets.
Vía J; Santamaría I; Pérez J
Neural Netw; 2007 Jan; 20(1):139-52. PubMed ID: 17113263
[TBL] [Abstract][Full Text] [Related]
38. Inter-individual single-trial classification of MEG data using M-CCA.
Michalke L; Dreyer AM; Borst JP; Rieger JW
Neuroimage; 2023 Jun; 273():120079. PubMed ID: 37023989
[TBL] [Abstract][Full Text] [Related]
39. Integrative analysis of gene expression and copy number alterations using canonical correlation analysis.
Soneson C; Lilljebjörn H; Fioretos T; Fontes M
BMC Bioinformatics; 2010 Apr; 11():191. PubMed ID: 20398334
[TBL] [Abstract][Full Text] [Related]
40. BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis.
Kumar M; Ellis CT; Lu Q; Zhang H; Capotă M; Willke TL; Ramadge PJ; Turk-Browne NB; Norman KA
PLoS Comput Biol; 2020 Jan; 16(1):e1007549. PubMed ID: 31940340
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
