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dbscan function  (MathWorks Inc)


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    MathWorks Inc dbscan function
    Dbscan Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2336 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+dbscan+function/Statistics+and+Machine+Learning+Toolbox/pm38310814-157-5-16
    Average 96 stars, based on 2336 article reviews
    dbscan function - by Bioz Stars, 2026-10
    96/100 stars

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    Related Articles

    Polymers:

    Article Title: Advanced Polymeric Physical Unclonable Functions: From Mechanistic Design and Computer Vision to Enhanced Effect of Stimuli.
    Article Snippet: Commodity fraud is a serious issue leading to significant financial losses for producers, marketers, and consumers.. The past anticounterfeiting and security systems were cloneable and predictable.. Today, development of advanced, multilayered, and unclonable systems focuses on providing stronger protection against counterfeiting.

    Software:

    Article Title: Advanced Polymeric Physical Unclonable Functions: From Mechanistic Design and Computer Vision to Enhanced Effect of Stimuli.
    Article Snippet: Commodity fraud is a serious issue leading to significant financial losses for producers, marketers, and consumers.. The past anticounterfeiting and security systems were cloneable and predictable.. Today, development of advanced, multilayered, and unclonable systems focuses on providing stronger protection against counterfeiting.

    other:

    Article Title: Dynamic modulation of social gaze by sex and familiarity in marmoset dyads
    Article Snippet: We used the maximum likelihood estimation method (mle() function in the Statistics and Machine Learning Toolbox in MATLAB) to fit a mixture of Gamma and Gaussian distributions.

    Article Title: Light-induced 3D-printing of DNA-based responsive hydrogels
    Article Snippet: An open-source algorithm has been used for the Shapiro-Wilk’s test [49], while the Student tests were picked from Statistics and Machine Learning Toolbox (MATLAB).

    Article Title: The plasma protease inhibitor ITIH4 forms oligomeric complexes and senses broader proteolytic activity
    Article Snippet: The curves were fitted globally using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks).



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    Cluster identification and classification workflow illustrated in a metastatic KM12L4a cell Individual localizations (ii) that make up the PALM-TIRF image (i) were clustered using the <t>DBSCAN</t> algorithm with minimum 17 <t>neighbors</t> <t>(MinPts)</t> and search radius (ε) 0.55. Retaining clusters with ≥50 localizations resulted in identification of individual clathrin clusters (iii). The designed cluster classification model (iv and v) classifies these as either a classical CCS (blue, indicated as CCP) or an alternative FCL (pink) based on 5 cluster-specific parameters (cluster area, number of localizations NrPts, eccentricity, perimeter, and the distance to the nearest neighboring cluster MinDistance) and a global threshold. Scale bars 5 μm, except scale bars of enlargements 1 μm.
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    Cluster identification and classification workflow illustrated in a metastatic KM12L4a cell Individual localizations (ii) that make up the PALM-TIRF image (i) were clustered using the <t>DBSCAN</t> algorithm with minimum 17 <t>neighbors</t> <t>(MinPts)</t> and search radius (ε) 0.55. Retaining clusters with ≥50 localizations resulted in identification of individual clathrin clusters (iii). The designed cluster classification model (iv and v) classifies these as either a classical CCS (blue, indicated as CCP) or an alternative FCL (pink) based on 5 cluster-specific parameters (cluster area, number of localizations NrPts, eccentricity, perimeter, and the distance to the nearest neighboring cluster MinDistance) and a global threshold. Scale bars 5 μm, except scale bars of enlargements 1 μm.
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    Cluster identification and classification workflow illustrated in a metastatic KM12L4a cell Individual localizations (ii) that make up the PALM-TIRF image (i) were clustered using the DBSCAN algorithm with minimum 17 neighbors (MinPts) and search radius (ε) 0.55. Retaining clusters with ≥50 localizations resulted in identification of individual clathrin clusters (iii). The designed cluster classification model (iv and v) classifies these as either a classical CCS (blue, indicated as CCP) or an alternative FCL (pink) based on 5 cluster-specific parameters (cluster area, number of localizations NrPts, eccentricity, perimeter, and the distance to the nearest neighboring cluster MinDistance) and a global threshold. Scale bars 5 μm, except scale bars of enlargements 1 μm.

    Journal: iScience

    Article Title: Flat clathrin lattices are linked to metastatic potential in colorectal cancer

    doi: 10.1016/j.isci.2023.107327

    Figure Lengend Snippet: Cluster identification and classification workflow illustrated in a metastatic KM12L4a cell Individual localizations (ii) that make up the PALM-TIRF image (i) were clustered using the DBSCAN algorithm with minimum 17 neighbors (MinPts) and search radius (ε) 0.55. Retaining clusters with ≥50 localizations resulted in identification of individual clathrin clusters (iii). The designed cluster classification model (iv and v) classifies these as either a classical CCS (blue, indicated as CCP) or an alternative FCL (pink) based on 5 cluster-specific parameters (cluster area, number of localizations NrPts, eccentricity, perimeter, and the distance to the nearest neighboring cluster MinDistance) and a global threshold. Scale bars 5 μm, except scale bars of enlargements 1 μm.

    Article Snippet: Then, the MATLAB DBSCAN function was executed for a minimum of 17 neighbors (MinPts) within a search radius (ε) of 0.55.

    Techniques:

    Journal: iScience

    Article Title: Flat clathrin lattices are linked to metastatic potential in colorectal cancer

    doi: 10.1016/j.isci.2023.107327

    Figure Lengend Snippet:

    Article Snippet: Then, the MATLAB DBSCAN function was executed for a minimum of 17 neighbors (MinPts) within a search radius (ε) of 0.55.

    Techniques: Recombinant, Software, Imaging, Microscopy, Fluorescence