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custom code harnessing matlab s bioinformatics toolbox  (MathWorks Inc)


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    MathWorks Inc custom code harnessing matlab s bioinformatics toolbox
    Custom Code Harnessing Matlab S Bioinformatics Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 382 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/bioinformatic+analysis+matlab+script/Parallel+Computing+Toolbox/pmc06035058-176-4-7
    Average 95 stars, based on 382 article reviews
    custom code harnessing matlab s bioinformatics toolbox - by Bioz Stars, 2026-09
    95/100 stars

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    Article Title: The geometry of pMHC‐coated nanoparticles and T‐cell receptor clusters governs the sensitivity‐specificity trade‐off in T‐cell response: a modeling investigation
    Article Snippet: Trials were run in parallel by means of the Parallel Computing Toolbox available in MATLAB, using servers from Compute Canada.

    Article Title: Deep computational photoacoustic mesoscopy through heterogeneous tissues enabled by scanning compensation and angular-spectrum enhancement
    Article Snippet: The DWAS-SAFT was implemented in MATLAB 2021a (Parallel Computing Toolbox) on a workstation equipped with an AMD Ryzen 5600 G CPU and 64 GB RAM.

    Article Title: A hybrid spiking convolutional neural framework with extreme learning machine for enhanced anomaly detection in network security.
    Article Snippet: The Neural Network Toolbox, Signal Processing Toolbox, and Parallel Computing Toolbox were used in MATLAB R2023b to carry out each experiment.

    Article Title: An Integrated Analysis of GLP-1R Agonist Mechanisms: Addressing Study Variations in Heterogeneous Cell Systems
    Article Snippet: The parameter estimation was done with 16 parallel workers (MATLAB Parallel Computing Toolbox) with a stopping criteria defined with max stall iterations (700), and a function tolerance (10 -5 ).

    Article Title: Diverse communities promote the coexistence of closely-related strains through emergent equalization and stabilization
    Article Snippet: Correlation coefficients: The correlation between α and β strain abundances was computed both across all strain pairs and restricted to coexisting pairs, using the standard formula: Lastly, simulations were parallelized across asymmetry parameter values (i.e., λ ) using MATLAB’s Parallel Computing Toolbox (parfor), with each worker independently generating interaction matrices, integrating the dynamical system, and computing summary statistics for a given λ value.

    Article Title: Protocol for identifying and comparing neuronal ensembles using different algorithms within a graphical user interface
    Article Snippet: MATLAB Parallel Computing Toolbox , MathWorks , https://www.mathworks.com/products/parallel-computing.html.

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    Article Title: Parameter Selection in Coupled Dynamical Systems for Tomographic Image Reconstruction.
    Article Snippet: .. The objective function 1K ∑ K k=1 d(·) was evaluated in parallel for k = 1, 2, . . . , K using the parfor construct in MATLAB’s Parallel Computing Toolbox to accelerate computation. ..



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    bioinformatic analysis matlab script  (MathWorks Inc)
    90
    MathWorks Inc bioinformatic analysis matlab script
    Expected outcomes for each step
    Bioinformatic Analysis Matlab Script, 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/bioinformatic+analysis+matlab+script/pmc08313752-349-7-9
    Average 90 stars, based on 1 article reviews
    bioinformatic analysis matlab script - by Bioz Stars, 2026-09
    90/100 stars
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    Expected outcomes for each step

    Journal: STAR Protocols

    Article Title: Integrating readout of somatic mutations in individual cells with single-cell transcriptional profiling

    doi: 10.1016/j.xpro.2021.100673

    Figure Lengend Snippet: Expected outcomes for each step

    Article Snippet: The computational pipeline consists of 1. the bioinformatic analysis MATLAB script and 2. two fastq files (read 1 and read 2) of locus specific amplicon libraries 2.

    Techniques: Magnetic Beads, Cell Counting, Nested PCR, Mutagenesis, cDNA Library Assay

    Accurate identification of the mutated cells from the amplicon libraries (A–C) (A) In a control experiment MOLT4 (WT cells) were mixed with UKE-1 cells (homozygous JAK2-V617F mutation) and ran through the experimental and analysis pipeline. The two cell populations could be distinguished based on their transcriptional profiles: two distinct clusters were seen when transcriptomes of the cells were visualized using UMAP. Marker genes (TCF7 shown here) were used to identify the clusters as either MOLT4 or UKE-1 cells. Cells in which a mutated JAK2 transcript (B) or a WT JAK2 transcript (C) were detected in the amplicon libraries are shown as colored points. All other cells are shown in gray. (D) All cells in which either a WT or mutated JAK2 transcript was detected in the amplicon libraries. JAK2 transcript were detected in ~ 4% of cells (249 out of 6563 cells). The rate of erroneously detecting a mutated transcript in a MOLT4 cell or a wildtype transcript in a UKE-1 cell in less than 1%. (E–G) Output plots from MATLAB analysis script. (E) Rank of unique indices. Index sequence can be found in MATLAB cell array ‘uniqueindices’. (F) Number of reads vs rank of unqiue molecules and the threshold for calling the detected molecules as either wildtype or mutated. (G) Number of reads vs rank of unique cells. (H) Example of top 200 most common Read 2 and its align results. Related to <xref ref-type=Figure 1 " width="100%" height="100%">

    Journal: STAR Protocols

    Article Title: Integrating readout of somatic mutations in individual cells with single-cell transcriptional profiling

    doi: 10.1016/j.xpro.2021.100673

    Figure Lengend Snippet: Accurate identification of the mutated cells from the amplicon libraries (A–C) (A) In a control experiment MOLT4 (WT cells) were mixed with UKE-1 cells (homozygous JAK2-V617F mutation) and ran through the experimental and analysis pipeline. The two cell populations could be distinguished based on their transcriptional profiles: two distinct clusters were seen when transcriptomes of the cells were visualized using UMAP. Marker genes (TCF7 shown here) were used to identify the clusters as either MOLT4 or UKE-1 cells. Cells in which a mutated JAK2 transcript (B) or a WT JAK2 transcript (C) were detected in the amplicon libraries are shown as colored points. All other cells are shown in gray. (D) All cells in which either a WT or mutated JAK2 transcript was detected in the amplicon libraries. JAK2 transcript were detected in ~ 4% of cells (249 out of 6563 cells). The rate of erroneously detecting a mutated transcript in a MOLT4 cell or a wildtype transcript in a UKE-1 cell in less than 1%. (E–G) Output plots from MATLAB analysis script. (E) Rank of unique indices. Index sequence can be found in MATLAB cell array ‘uniqueindices’. (F) Number of reads vs rank of unqiue molecules and the threshold for calling the detected molecules as either wildtype or mutated. (G) Number of reads vs rank of unique cells. (H) Example of top 200 most common Read 2 and its align results. Related to Figure 1

    Article Snippet: The computational pipeline consists of 1. the bioinformatic analysis MATLAB script and 2. two fastq files (read 1 and read 2) of locus specific amplicon libraries 2.

    Techniques: Amplification, Control, Mutagenesis, Marker, Sequencing