gaussian mixture distribution model Search Results


gaussian mixture model  (Optik GmbH)
90
Optik GmbH gaussian mixture model
Gaussian Mixture Model, supplied by Optik GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gaussian mixture model  (Myspace LLC)
90
Myspace LLC gaussian mixture model
Gaussian Mixture Model, supplied by Myspace LLC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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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
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gaussian mixture models  (Hasegawa Co Ltd)
90
Hasegawa Co Ltd gaussian mixture models
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.
Gaussian Mixture Models, supplied by Hasegawa Co Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gaussian mixture models - by Bioz Stars, 2026-03
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spatiotemporal clustering using gaussian processes embedded in a mixture model  (Environmetrics Pty Ltd)
90
Environmetrics Pty Ltd spatiotemporal clustering using gaussian processes embedded in a 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.
Spatiotemporal Clustering Using Gaussian Processes Embedded In A Mixture Model, supplied by Environmetrics Pty Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gaussian softmax distribution (gsm) model  (SoftMax Inc)
90
SoftMax Inc gaussian softmax distribution (gsm) 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.
Gaussian Softmax Distribution (Gsm) Model, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gaussian mixture model (gmm)  (Baidu Inc)
90
Baidu Inc gaussian mixture model (gmm)
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.
Gaussian Mixture Model (Gmm), supplied by Baidu Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gaussian mixture model (gmm) - by Bioz Stars, 2026-03
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gaussian mixture model  (MultiFit Tiernahrungs)
90
MultiFit Tiernahrungs 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.
Gaussian Mixture Model, supplied by MultiFit Tiernahrungs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gaussian mixture model  (Microsensors Inc)
90
Microsensors Inc 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.
Gaussian Mixture Model, supplied by Microsensors 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/gaussian mixture model/product/Microsensors Inc
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r package for fitting gaussian mixture models on incomplete data  (Cold Spring Harbor Laboratory Meetings)
90
Cold Spring Harbor Laboratory Meetings r package for fitting gaussian mixture models on incomplete data
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.
R Package For Fitting Gaussian Mixture Models On Incomplete Data, supplied by Cold Spring Harbor Laboratory Meetings, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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r package for fitting gaussian mixture models on incomplete data - by Bioz Stars, 2026-03
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acoustic fall detection using gaussian mixture models and gmm supervectors  (Hasegawa Co Ltd)
90
Hasegawa Co Ltd acoustic fall detection using gaussian mixture models and gmm supervectors
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.
Acoustic Fall Detection Using Gaussian Mixture Models And Gmm Supervectors, supplied by Hasegawa Co Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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non-parametric bayesian approach (an infinite gaussian mixture model)  (Janssen)
90
Janssen non-parametric bayesian approach (an infinite 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.
Non Parametric Bayesian Approach (An Infinite Gaussian Mixture Model), supplied by Janssen, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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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