Review



bimodal gaussian mixture model  (GraphPad Software Inc)


Bioz Verified Symbol GraphPad Software Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    GraphPad Software Inc bimodal gaussian mixture model
    GRE- and ARE-motif effect on mRNA stability Microarray-based mRNA half-life data (see Materials and Methods for details) were stratified according to the experimental condition, transcript type and half-life classification (short vs. long half-lives, based upon a <t>Gaussian-mixture</t> model fit. (A) The histograms of log-2 values (columns) of mRNA half-lives in each gene category (Control, ARE-genes and GRE-genes) were obtained and fitted using a bi-modal Gaussian mixture model (line). The control set represent mRNAs with neither ARE nor GRE. A representative model under the anti-CD3 experimental condition is shown. (B) The fraction of short half-life transcripts out of the total within each stratum. Data are Mean ± SEM and represent fitted parameter uncertainties. For each stimulus, ARE-mRNAs and GRE-mRNAs results were compared with the Control using the unpaired t-test with Bonferroni correction for multiple testing. **p < 0.01 and *p < 0.05. Using the 2-way ANOVA test, the effect of the motif (Control, ARE+ or GRE+) was found to be significant at p < 0.01. (C) A simpler classification scheme based on the arbitrary cutoff of 64 (26) minutes to classify short and long half-life transcripts yields essentially the same results as in (A). (D) The proportions of control mRNAs, ARE-mRNAs and GRE-mRNAs that do not change their stability patterns (non-switching class). p < 0.001 using chi-square analysis.
    Bimodal Gaussian Mixture Model, supplied by GraphPad Software 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/result/bimodal gaussian mixture model/product/GraphPad Software Inc
    Average 90 stars, based on 1 article reviews
    bimodal gaussian mixture model - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "Global assessment of GU-rich regulatory content and function in the human transcriptome"

    Article Title: Global assessment of GU-rich regulatory content and function in the human transcriptome

    Journal: RNA Biology

    doi: 10.4161/rna.8.4.16283

    GRE- and ARE-motif effect on mRNA stability Microarray-based mRNA half-life data (see Materials and Methods for details) were stratified according to the experimental condition, transcript type and half-life classification (short vs. long half-lives, based upon a Gaussian-mixture model fit. (A) The histograms of log-2 values (columns) of mRNA half-lives in each gene category (Control, ARE-genes and GRE-genes) were obtained and fitted using a bi-modal Gaussian mixture model (line). The control set represent mRNAs with neither ARE nor GRE. A representative model under the anti-CD3 experimental condition is shown. (B) The fraction of short half-life transcripts out of the total within each stratum. Data are Mean ± SEM and represent fitted parameter uncertainties. For each stimulus, ARE-mRNAs and GRE-mRNAs results were compared with the Control using the unpaired t-test with Bonferroni correction for multiple testing. **p < 0.01 and *p < 0.05. Using the 2-way ANOVA test, the effect of the motif (Control, ARE+ or GRE+) was found to be significant at p < 0.01. (C) A simpler classification scheme based on the arbitrary cutoff of 64 (26) minutes to classify short and long half-life transcripts yields essentially the same results as in (A). (D) The proportions of control mRNAs, ARE-mRNAs and GRE-mRNAs that do not change their stability patterns (non-switching class). p < 0.001 using chi-square analysis.
    Figure Legend Snippet: GRE- and ARE-motif effect on mRNA stability Microarray-based mRNA half-life data (see Materials and Methods for details) were stratified according to the experimental condition, transcript type and half-life classification (short vs. long half-lives, based upon a Gaussian-mixture model fit. (A) The histograms of log-2 values (columns) of mRNA half-lives in each gene category (Control, ARE-genes and GRE-genes) were obtained and fitted using a bi-modal Gaussian mixture model (line). The control set represent mRNAs with neither ARE nor GRE. A representative model under the anti-CD3 experimental condition is shown. (B) The fraction of short half-life transcripts out of the total within each stratum. Data are Mean ± SEM and represent fitted parameter uncertainties. For each stimulus, ARE-mRNAs and GRE-mRNAs results were compared with the Control using the unpaired t-test with Bonferroni correction for multiple testing. **p < 0.01 and *p < 0.05. Using the 2-way ANOVA test, the effect of the motif (Control, ARE+ or GRE+) was found to be significant at p < 0.01. (C) A simpler classification scheme based on the arbitrary cutoff of 64 (26) minutes to classify short and long half-life transcripts yields essentially the same results as in (A). (D) The proportions of control mRNAs, ARE-mRNAs and GRE-mRNAs that do not change their stability patterns (non-switching class). p < 0.001 using chi-square analysis.

    Techniques Used: Microarray, Control



    Similar Products

    bimodal gaussian mixture model  (GraphPad Software Inc)
    90
    GraphPad Software Inc bimodal gaussian mixture model
    GRE- and ARE-motif effect on mRNA stability Microarray-based mRNA half-life data (see Materials and Methods for details) were stratified according to the experimental condition, transcript type and half-life classification (short vs. long half-lives, based upon a <t>Gaussian-mixture</t> model fit. (A) The histograms of log-2 values (columns) of mRNA half-lives in each gene category (Control, ARE-genes and GRE-genes) were obtained and fitted using a bi-modal Gaussian mixture model (line). The control set represent mRNAs with neither ARE nor GRE. A representative model under the anti-CD3 experimental condition is shown. (B) The fraction of short half-life transcripts out of the total within each stratum. Data are Mean ± SEM and represent fitted parameter uncertainties. For each stimulus, ARE-mRNAs and GRE-mRNAs results were compared with the Control using the unpaired t-test with Bonferroni correction for multiple testing. **p < 0.01 and *p < 0.05. Using the 2-way ANOVA test, the effect of the motif (Control, ARE+ or GRE+) was found to be significant at p < 0.01. (C) A simpler classification scheme based on the arbitrary cutoff of 64 (26) minutes to classify short and long half-life transcripts yields essentially the same results as in (A). (D) The proportions of control mRNAs, ARE-mRNAs and GRE-mRNAs that do not change their stability patterns (non-switching class). p < 0.001 using chi-square analysis.
    Bimodal Gaussian Mixture Model, supplied by GraphPad Software 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/result/bimodal gaussian mixture model/product/GraphPad Software Inc
    Average 90 stars, based on 1 article reviews
    bimodal gaussian mixture model - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    GRE- and ARE-motif effect on mRNA stability Microarray-based mRNA half-life data (see Materials and Methods for details) were stratified according to the experimental condition, transcript type and half-life classification (short vs. long half-lives, based upon a Gaussian-mixture model fit. (A) The histograms of log-2 values (columns) of mRNA half-lives in each gene category (Control, ARE-genes and GRE-genes) were obtained and fitted using a bi-modal Gaussian mixture model (line). The control set represent mRNAs with neither ARE nor GRE. A representative model under the anti-CD3 experimental condition is shown. (B) The fraction of short half-life transcripts out of the total within each stratum. Data are Mean ± SEM and represent fitted parameter uncertainties. For each stimulus, ARE-mRNAs and GRE-mRNAs results were compared with the Control using the unpaired t-test with Bonferroni correction for multiple testing. **p < 0.01 and *p < 0.05. Using the 2-way ANOVA test, the effect of the motif (Control, ARE+ or GRE+) was found to be significant at p < 0.01. (C) A simpler classification scheme based on the arbitrary cutoff of 64 (26) minutes to classify short and long half-life transcripts yields essentially the same results as in (A). (D) The proportions of control mRNAs, ARE-mRNAs and GRE-mRNAs that do not change their stability patterns (non-switching class). p < 0.001 using chi-square analysis.

    Journal: RNA Biology

    Article Title: Global assessment of GU-rich regulatory content and function in the human transcriptome

    doi: 10.4161/rna.8.4.16283

    Figure Lengend Snippet: GRE- and ARE-motif effect on mRNA stability Microarray-based mRNA half-life data (see Materials and Methods for details) were stratified according to the experimental condition, transcript type and half-life classification (short vs. long half-lives, based upon a Gaussian-mixture model fit. (A) The histograms of log-2 values (columns) of mRNA half-lives in each gene category (Control, ARE-genes and GRE-genes) were obtained and fitted using a bi-modal Gaussian mixture model (line). The control set represent mRNAs with neither ARE nor GRE. A representative model under the anti-CD3 experimental condition is shown. (B) The fraction of short half-life transcripts out of the total within each stratum. Data are Mean ± SEM and represent fitted parameter uncertainties. For each stimulus, ARE-mRNAs and GRE-mRNAs results were compared with the Control using the unpaired t-test with Bonferroni correction for multiple testing. **p < 0.01 and *p < 0.05. Using the 2-way ANOVA test, the effect of the motif (Control, ARE+ or GRE+) was found to be significant at p < 0.01. (C) A simpler classification scheme based on the arbitrary cutoff of 64 (26) minutes to classify short and long half-life transcripts yields essentially the same results as in (A). (D) The proportions of control mRNAs, ARE-mRNAs and GRE-mRNAs that do not change their stability patterns (non-switching class). p < 0.001 using chi-square analysis.

    Article Snippet: We proceeded to estimate the ratio of the short set to the long set by fitting the histogram data with a bimodal Gaussian Mixture model using GraphPad Prism software functions.

    Techniques: Microarray, Control