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least squares method in the matlab statistics and machine learning toolbox  (MathWorks Inc)


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    MathWorks Inc least squares method in the matlab statistics and machine learning toolbox
    Least Squares Method In The Matlab Statistics And Machine Learning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/machine+learning+method/pmc12267541-111-21-21
    Average 90 stars, based on 1 article reviews
    least squares method in the matlab statistics and machine learning toolbox - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma
    Article Snippet: Additionally, we performed linear regression on the scan-rescan data to estimate the regression line using the least squares method in the MATLAB Statistics and Machine Learning Toolbox.

    Article Title: Validation of Left Ventricular High Frame Rate Echo-Particle Image Velocimetry against 4D Flow MRI in Patients.
    Article Snippet: Statistical analysis was performed using Matlab Statistics and Machine Learning Toolbox.

    Article Title: Resolving Phenotypic Variability in Mitochondrial Diseases: Preliminary Findings of a Proteomic Approach
    Article Snippet: Fold Change (FC) ratios and p -values were calculated using Matlab Statistics (9.13 version) and Machine Learning Toolbox and Bioinformatics Toolbox ( https://it.mathworks.com/products/statistics.html ; https://it.mathworks.com/products/bioinfo.html , accessed on 22 February 2024; R2022b version, MathWorks Inc., Natick, MA, USA).

    Article Title: Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks.
    Article Snippet: Matlab® Statistics and Machine Learning Toolbox (Matlab 2021b toolbox) was used to implement the described architecture.

    Article Title: DWORF expression is reduced in a large animal model of Duchenne muscular dystrophy.
    Article Snippet: Statistical analysis was performed using the Matlab Statistics and Machine Learning Toolbox (V24.1; Matlab 2024a, MathWorks Inc., Natick, MA, USA).

    Article Title: Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation
    Article Snippet: Algorithm prediction was undertaken individually for each of the 7 outcome models using the MATLAB Statistics and Machine Learning Toolbox (Release 2023b).

    Article Title: Distinct 11C-ER176 PET Neuroinflammatory Profiles and Tau Colocalization in Progressive Apraxia of Speech With and Without Parkinson-plus Syndrome
    Article Snippet: Background: Progressive apraxia of speech (PAOS) is a neurodegenerative disorder most commonly arising from a 4-repeat tauopathy that affects the programming or planning of speech, although many patients develop a Parkinson-plus syndrome.. The role of neuroinflammation as a disease mechanism is unknown.. We investigated the spatial pattern of neuroinflammation in PAOS using PET and evaluated whether it is associated with disease severity, presence of Parkinson-plus features, and tau PET uptake.

    Generated:

    Article Title: Relationship between tau-PET and quantitative susceptibility mapping in atypical Alzheimer’s disease
    Article Snippet: .. These plots were generated using MATLAB Statistics and Machine Learning Toolbox (R2022a). ..



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    A comparison of genomic prediction performance of the naïve ensemble-average (ensemble) model vs each of the individual genomic prediction models in violin plots. The width of the violins indicates the distribution of the metric values for predictions from all combinations of the 5 RIL populations, 3 training-test ratios, and 500 random samples. The performance of genomic prediction models was measured with a) the Pearson correlation and b) MSE. The orange represents the performance of classical models (rrBLUP, BayesB, and RKHS) while the green represents machine learning models (RF, SVR, and GAT). The red is the performance of the ensemble. Box plots within the violin plots represent the median metric value (white line) and the interquartile range (black box) with whiskers extending 1.5 times the interquartile range.

    Journal: G3: Genes | Genomes | Genetics

    Article Title: Improved genomic prediction performance with ensembles of diverse models

    doi: 10.1093/g3journal/jkaf048

    Figure Lengend Snippet: A comparison of genomic prediction performance of the naïve ensemble-average (ensemble) model vs each of the individual genomic prediction models in violin plots. The width of the violins indicates the distribution of the metric values for predictions from all combinations of the 5 RIL populations, 3 training-test ratios, and 500 random samples. The performance of genomic prediction models was measured with a) the Pearson correlation and b) MSE. The orange represents the performance of classical models (rrBLUP, BayesB, and RKHS) while the green represents machine learning models (RF, SVR, and GAT). The red is the performance of the ensemble. Box plots within the violin plots represent the median metric value (white line) and the interquartile range (black box) with whiskers extending 1.5 times the interquartile range.

    Article Snippet: From the various machine learning methods, we selected RF , SVR ( Drucker et al. 1996 ), and GAT ( Velickovic et al. 2017 ) for our investigation of ensemble prediction.

    Techniques: Comparison