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simpls algorithm  (MathWorks Inc)


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    MathWorks Inc simpls algorithm
    Simpls Algorithm, 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/simpls+algorithm/pm39889810-122-5-15
    Average 90 stars, based on 1 article reviews
    simpls algorithm - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China
    Article Snippet: We used the SIMPLS algorithm [60] to carry out the PLSR procedure in MATLAB (MathWorks, Inc. Natick, MA, USA).

    Article Title: A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity
    Article Snippet: SIMPLS algorithm was used to solve PLSR as implemented in MATLAB R2016a (plsregress function).

    Article Title: A Cross-Cultural Analysis of the Influence of Timbre on Affect Perception in Western Classical Music and Chinese Music Traditions
    Article Snippet: The SIMPLS algorithm ( ) was applied to the PLSR and implemented in MATLAB.

    Article Title: Analysis and Synthesis of Natural Texture Perception From Visual Evoked Potentials
    Article Snippet: We implemented the SIMPLS algorithm through the MATLAB function “plsregress”.

    Article Title: Methane detection to 1 ppm using machine learning analysis of atmospheric pressure plasma optical emission spectra
    Article Snippet: PLS-DA is implemented using the SIMPLS algorithm in Matlab [57].

    Article Title: The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy.
    Article Snippet: Objective: Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor.. Aortic expansion is a major factor that determines patient long-term survival.. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT.

    Article Title: Structural damage-driven brain compensation among near-centenarians and centenarians without dementia.
    Article Snippet: Compensation has been proposed as a mechanism to explain how individuals in very old age remain able to maintain normal cognitive functioning.. Previous studies have provided evidence on the role of increasing functional connectivity as a compensatory mechanism for age-related white matter damage.. However, we lack direct investigation into how these mechanisms contribute to the preservation of cognition in the very old population.

    Construct:

    Article Title: Optimizing a Standard Spectral Measurement Protocol to Enhance the Quality of Soil Spectra: Exploration of Key Variables in Lab-Based VNIR-SWIR Spectral Measurement
    Article Snippet: .. For the computations, we employed the SIMPLS algorithm in MATLAB (MathWorks, Inc. USA), which constructs the original independent variables (spectra) into orthogonal PLS components (also known as the latent variables) that represent linear combinations of them and further relates the dependent variables with the new PLS components by linear regression [46]. ..



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    ( a ) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. ( b ) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in ( a ). Of note, ‘omitted’ represents reads that did not cover the focused position. ( c ) Probability logo plot. Statistical significance (−log 10 [p-value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in ( a ) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. ( d ) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the probability of incorrect classification of a read was calculated assuming that two plasmids that differ by the indicated base(s) were mixed. ( e ) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the indicated number of reads were generated in silico, and the consensus base calling was calculated using Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You <t>(SAVEMONEY).</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling. Figure 6—source code 1. Source code used to make . Figure 6—source code 2. Source code used to make . Figure 6—source data 1. Csv file containing raw data used to make . Figure 6—source data 2. Csv file containing raw data used to make . Figure 6—source data 3. Text file containing raw data used to make . Figure 6—source data 4. Csv file containing raw data used to make .
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    ( a ) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. ( b ) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in ( a ). Of note, ‘omitted’ represents reads that did not cover the focused position. ( c ) Probability logo plot. Statistical significance (−log 10 [p-value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in ( a ) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. ( d ) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the probability of incorrect classification of a read was calculated assuming that two plasmids that differ by the indicated base(s) were mixed. ( e ) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the indicated number of reads were generated in silico, and the consensus base calling was calculated using Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You <t>(SAVEMONEY).</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling. Figure 6—source code 1. Source code used to make . Figure 6—source code 2. Source code used to make . Figure 6—source data 1. Csv file containing raw data used to make . Figure 6—source data 2. Csv file containing raw data used to make . Figure 6—source data 3. Text file containing raw data used to make . Figure 6—source data 4. Csv file containing raw data used to make .
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    ( a ) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. ( b ) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in ( a ). Of note, ‘omitted’ represents reads that did not cover the focused position. ( c ) Probability logo plot. Statistical significance (−log 10 [p-value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in ( a ) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. ( d ) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the probability of incorrect classification of a read was calculated assuming that two plasmids that differ by the indicated base(s) were mixed. ( e ) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the indicated number of reads were generated in silico, and the consensus base calling was calculated using Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You <t>(SAVEMONEY).</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling. Figure 6—source code 1. Source code used to make . Figure 6—source code 2. Source code used to make . Figure 6—source data 1. Csv file containing raw data used to make . Figure 6—source data 2. Csv file containing raw data used to make . Figure 6—source data 3. Text file containing raw data used to make . Figure 6—source data 4. Csv file containing raw data used to make .
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    ( a ) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. ( b ) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in ( a ). Of note, ‘omitted’ represents reads that did not cover the focused position. ( c ) Probability logo plot. Statistical significance (−log 10 [p-value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in ( a ) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. ( d ) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the probability of incorrect classification of a read was calculated assuming that two plasmids that differ by the indicated base(s) were mixed. ( e ) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the indicated number of reads were generated in silico, and the consensus base calling was calculated using Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You <t>(SAVEMONEY).</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling. Figure 6—source code 1. Source code used to make . Figure 6—source code 2. Source code used to make . Figure 6—source data 1. Csv file containing raw data used to make . Figure 6—source data 2. Csv file containing raw data used to make . Figure 6—source data 3. Text file containing raw data used to make . Figure 6—source data 4. Csv file containing raw data used to make .
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    ( a ) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. ( b ) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in ( a ). Of note, ‘omitted’ represents reads that did not cover the focused position. ( c ) Probability logo plot. Statistical significance (−log 10 [p-value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in ( a ) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. ( d ) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the probability of incorrect classification of a read was calculated assuming that two plasmids that differ by the indicated base(s) were mixed. ( e ) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the indicated number of reads were generated in silico, and the consensus base calling was calculated using Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You <t>(SAVEMONEY).</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling. Figure 6—source code 1. Source code used to make . Figure 6—source code 2. Source code used to make . Figure 6—source data 1. Csv file containing raw data used to make . Figure 6—source data 2. Csv file containing raw data used to make . Figure 6—source data 3. Text file containing raw data used to make . Figure 6—source data 4. Csv file containing raw data used to make .
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    ( a ) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. ( b ) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in ( a ). Of note, ‘omitted’ represents reads that did not cover the focused position. ( c ) Probability logo plot. Statistical significance (−log 10 [p-value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in ( a ) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. ( d ) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the probability of incorrect classification of a read was calculated assuming that two plasmids that differ by the indicated base(s) were mixed. ( e ) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the indicated number of reads were generated in silico, and the consensus base calling was calculated using Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY). The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling. Figure 6—source code 1. Source code used to make . Figure 6—source code 2. Source code used to make . Figure 6—source data 1. Csv file containing raw data used to make . Figure 6—source data 2. Csv file containing raw data used to make . Figure 6—source data 3. Text file containing raw data used to make . Figure 6—source data 4. Csv file containing raw data used to make .

    Journal: eLife

    Article Title: Barcode-free multiplex plasmid sequencing using Bayesian analysis and nanopore sequencing

    doi: 10.7554/eLife.88794

    Figure Lengend Snippet: ( a ) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. ( b ) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in ( a ). Of note, ‘omitted’ represents reads that did not cover the focused position. ( c ) Probability logo plot. Statistical significance (−log 10 [p-value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in ( a ) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. ( d ) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the probability of incorrect classification of a read was calculated assuming that two plasmids that differ by the indicated base(s) were mixed. ( e ) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the ‘worst-case scenario’, i.e., top panel in ( b ), the indicated number of reads were generated in silico, and the consensus base calling was calculated using Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY). The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling. Figure 6—source code 1. Source code used to make . Figure 6—source code 2. Source code used to make . Figure 6—source data 1. Csv file containing raw data used to make . Figure 6—source data 2. Csv file containing raw data used to make . Figure 6—source data 3. Text file containing raw data used to make . Figure 6—source data 4. Csv file containing raw data used to make .

    Article Snippet: Here, we have developed a barcode-free, easy-to-use computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides users to pool samples for nanopore sequencing and effectively reduces sequencing costs to as low as $2.50 (USD) per plasmid, which is about twice less expensive than one reaction of Sanger sequencing.

    Techniques: Nanopore Sequencing, Generated, In Silico, Multiplexing

    Journal: eLife

    Article Title: Barcode-free multiplex plasmid sequencing using Bayesian analysis and nanopore sequencing

    doi: 10.7554/eLife.88794

    Figure Lengend Snippet:

    Article Snippet: Here, we have developed a barcode-free, easy-to-use computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides users to pool samples for nanopore sequencing and effectively reduces sequencing costs to as low as $2.50 (USD) per plasmid, which is about twice less expensive than one reaction of Sanger sequencing.

    Techniques: Recombinant, Plasmid Preparation, Mutagenesis, Construct, Software