gaussian mixture 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
https://www.bioz.com/product/gaussian+mixture+model/pm30123948-233-16-19?v=Optik+GmbH
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gaussian mixture models (gmms)  (InContext Inc)
90
InContext Inc gaussian mixture models (gmms)
Gaussian Mixture Models (Gmms), supplied by InContext 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/gaussian+mixture+model/pm40613479-55-2-30?v=InContext+Inc
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gaussian mixture model  (Addinsoft inc)
90
Addinsoft inc gaussian mixture model
Gaussian Mixture Model, supplied by Addinsoft 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/gaussian+mixture+model/pmc05865209-324-1-9?v=Addinsoft+inc
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gaussian mixture model - by Bioz Stars, 2026-08
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two gaussians mixture model normalmixem procedure  (RStudio)
90
RStudio two gaussians mixture model normalmixem procedure
Two Gaussians Mixture Model Normalmixem Procedure, supplied by RStudio, 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/gaussian+mixture+model/pmc05944918-341-20-26?v=RStudio
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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
https://www.bioz.com/product/gaussian+mixture+model/pmc03225982-357-21-26?v=GraphPad+Software+Inc
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bimodal gaussian mixture model - by Bioz Stars, 2026-08
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gaussian mixture model  (IEEE Access)
90
IEEE Access 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 IEEE Access, 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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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
https://www.bioz.com/product/gaussian+mixture+model/pm36553079-534-8-10?v=Environmetrics+Pty+Ltd
Average 90 stars, based on 1 article reviews
spatiotemporal clustering using gaussian processes embedded in a mixture model - by Bioz Stars, 2026-08
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gaussian mixture model  (Myspace LLC)
90
Myspace LLC 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 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
https://www.bioz.com/product/gaussian+mixture+model/10__26599_slash_tst__2018__9010098-521-1-0?v=Myspace+LLC
Average 90 stars, based on 1 article reviews
gaussian mixture model - by Bioz Stars, 2026-08
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gaussian mixture models  (Daeyoung Inc)
90
Daeyoung Inc 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 Daeyoung 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/gaussian+mixture+model/10__1080_slash_10298436__2017__1309193-23-47-49?v=Daeyoung+Inc
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gaussian mixture models - by Bioz Stars, 2026-08
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gaussian mixture model (gmm) procedure of sas® visual data mining and machine learning 8.5 procedures  (SAS institute)
90
SAS institute gaussian mixture model (gmm) procedure of sas® visual data mining and machine learning 8.5 procedures
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) Procedure Of Sas® Visual Data Mining And Machine Learning 8.5 Procedures, supplied by SAS institute, 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/gaussian+mixture+model/us11416712-264-21-24?v=SAS+institute
Average 90 stars, based on 1 article reviews
gaussian mixture model (gmm) procedure of sas® visual data mining and machine learning 8.5 procedures - by Bioz Stars, 2026-08
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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
https://www.bioz.com/product/gaussian+mixture+model/pm25914056-31-8-0?v=MultiFit+Tiernahrungs
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gaussian mixture model - by Bioz Stars, 2026-08
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gaussian mixture variational autoencoder model  (IEEE Access)
90
IEEE Access gaussian mixture variational autoencoder 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 Variational Autoencoder Model, supplied by IEEE Access, 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/gaussian+mixture+model/pm38417353-391-11-16?v=IEEE+Access
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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