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mixture model-based clustering with temporal dimension (tmc)  (Technical Manufacturing Company)


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    Technical Manufacturing Company mixture model-based clustering with temporal dimension (tmc)
    Mixture Model Based Clustering With Temporal Dimension (Tmc), supplied by Technical Manufacturing Company, 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/model-based+clustering/mixture+model+based+clustering+with+temporal+dimension++tmc+/ppr0299394-2-6-7
    Average 90 stars, based on 1 article reviews
    mixture model-based clustering with temporal dimension (tmc) - by Bioz Stars, 2026-11
    90/100 stars

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    Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.

    Journal: Vaccines

    Article Title: From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

    doi: 10.3390/vaccines8010138

    Figure Lengend Snippet: Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.

    Article Snippet: , SWIFT , Matlab , Clustering method based on splitting and merging of Gaussian mixture models , [ ] .

    Techniques: Software, Flow Cytometry, Control, Construct, Biomarker Discovery, Sampling, Diffusion-based Assay, Preserving

    Data transformations. ( a ) A random data set was generated using two gaussian distributions, centered at 10 2 (10000 cells) and 2*10 3 (1000 cells), and with standard deviation equal to 10 2 and 10 3 , respectively. The probability histogram is shown on a linear scale. ( b ) Arcsinh transformation. The parameter λ defines both the width of the linear region, and its slope. The transforming function is approximately linear for λ close to zero, while it approaches logarithmic transformations when x >> λ or when x << −λ. ( c ) Logicle transformation. The shape of the transforming function is defined by the parameters M, A, and W, which can be intuitively interpreted respectively as the number of decades, the number of negative decades, and the width of the linear region. ( d ) Probability histogram of the data in a transformed with the arcsinh function. ( e ) Probability histogram of the data in a transformed with the logicle function.

    Journal: Vaccines

    Article Title: From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

    doi: 10.3390/vaccines8010138

    Figure Lengend Snippet: Data transformations. ( a ) A random data set was generated using two gaussian distributions, centered at 10 2 (10000 cells) and 2*10 3 (1000 cells), and with standard deviation equal to 10 2 and 10 3 , respectively. The probability histogram is shown on a linear scale. ( b ) Arcsinh transformation. The parameter λ defines both the width of the linear region, and its slope. The transforming function is approximately linear for λ close to zero, while it approaches logarithmic transformations when x >> λ or when x << −λ. ( c ) Logicle transformation. The shape of the transforming function is defined by the parameters M, A, and W, which can be intuitively interpreted respectively as the number of decades, the number of negative decades, and the width of the linear region. ( d ) Probability histogram of the data in a transformed with the arcsinh function. ( e ) Probability histogram of the data in a transformed with the logicle function.

    Article Snippet: , SWIFT , Matlab , Clustering method based on splitting and merging of Gaussian mixture models , [ ] .

    Techniques: Generated, Standard Deviation, Transformation Assay