Custom Tissue Arrays Search Results


customized mouse oligo gearray microarray membranes  (SuperArray Bioscience Corporation)
90
SuperArray Bioscience Corporation customized mouse oligo gearray microarray membranes
Customized Mouse Oligo Gearray Microarray Membranes, supplied by SuperArray Bioscience Corporation, 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/Custom+Tissue+Arrays/pmc02593420-64-10-23?v=SuperArray+Bioscience+Corporation
Average 90 stars, based on 1 article reviews
customized mouse oligo gearray microarray membranes - by Bioz Stars, 2026-08
90/100 stars
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customized whole-genome high-density microarrays  (Agendia BV)
90
Agendia BV customized whole-genome high-density microarrays
Available gene expression assays predictive of prognosis in early-stage CRC.
Customized Whole Genome High Density Microarrays, supplied by Agendia BV, 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/Custom+Tissue+Arrays/pmc10216373-0-22-20?v=Agendia+BV
Average 90 stars, based on 1 article reviews
customized whole-genome high-density microarrays - by Bioz Stars, 2026-08
90/100 stars
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tissue microarray  (Asterand Inc)
90
Asterand Inc tissue microarray
Available gene expression assays predictive of prognosis in early-stage CRC.
Tissue Microarray, supplied by Asterand 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/Custom+Tissue+Arrays/pmc04203382-59-1-22?v=Asterand+Inc
Average 90 stars, based on 1 article reviews
tissue microarray - by Bioz Stars, 2026-08
90/100 stars
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custom c botulinum type a1 strain atcc 3502 comparative genomic hybridization arrays  (ATCC)
96
ATCC custom c botulinum type a1 strain atcc 3502 comparative genomic hybridization arrays
Available gene expression assays predictive of prognosis in early-stage CRC.
Custom C Botulinum Type A1 Strain Atcc 3502 Comparative Genomic Hybridization Arrays, supplied by ATCC, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/Custom+Tissue+Arrays/pmc03038992-89-1-7?v=ATCC
Average 96 stars, based on 1 article reviews
custom c botulinum type a1 strain atcc 3502 comparative genomic hybridization arrays - by Bioz Stars, 2026-08
96/100 stars
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tissue arrays (ffpe)  (BioChain Institute)
90
BioChain Institute tissue arrays (ffpe)
Available gene expression assays predictive of prognosis in early-stage CRC.
Tissue Arrays (Ffpe), supplied by BioChain 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/Custom+Tissue+Arrays/pmc07093180-75-23-42?v=BioChain+Institute
Average 90 stars, based on 1 article reviews
tissue arrays (ffpe) - by Bioz Stars, 2026-08
90/100 stars
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custom human mirna array data  (CapitalBio Corporation)
90
CapitalBio Corporation custom human mirna array data
(A) Autoencoder architecture used to integrate 3 omics <t>of</t> <t>HCC</t> data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and <t>miRNA</t> features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.
Custom Human Mirna Array Data, supplied by CapitalBio Corporation, 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/Custom+Tissue+Arrays/pmc06050171-223-12-11?v=CapitalBio+Corporation
Average 90 stars, based on 1 article reviews
custom human mirna array data - by Bioz Stars, 2026-08
90/100 stars
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clear 48-well falcon tissue culture plate  (Corning Life Sciences)
90
Corning Life Sciences clear 48-well falcon tissue culture plate
(A) Autoencoder architecture used to integrate 3 omics <t>of</t> <t>HCC</t> data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and <t>miRNA</t> features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.
Clear 48 Well Falcon Tissue Culture Plate, supplied by Corning Life Sciences, 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/Custom+Tissue+Arrays/pmc04373973-76-34-39?v=Corning+Life+Sciences
Average 90 stars, based on 1 article reviews
clear 48-well falcon tissue culture plate - by Bioz Stars, 2026-08
90/100 stars
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custom made b cereus atcc 10987 microarrays  (ATCC)
96
ATCC custom made b cereus atcc 10987 microarrays
(A) Autoencoder architecture used to integrate 3 omics <t>of</t> <t>HCC</t> data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and <t>miRNA</t> features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.
Custom Made B Cereus Atcc 10987 Microarrays, supplied by ATCC, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/Custom+Tissue+Arrays/pm20074238-181-13-16?v=ATCC
Average 96 stars, based on 1 article reviews
custom made b cereus atcc 10987 microarrays - by Bioz Stars, 2026-08
96/100 stars
  Buy from Supplier
oligonucleotide microarray  (NimbleGen Systems GmbH)
90
NimbleGen Systems GmbH oligonucleotide microarray
(A) Autoencoder architecture used to integrate 3 omics <t>of</t> <t>HCC</t> data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and <t>miRNA</t> features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.
Oligonucleotide Microarray, supplied by NimbleGen Systems 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/Custom+Tissue+Arrays/pm21435035-337-1-20?v=NimbleGen+Systems+GmbH
Average 90 stars, based on 1 article reviews
oligonucleotide microarray - by Bioz Stars, 2026-08
90/100 stars
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6-well tissue culture plate  (Becton Dickinson)
90
Becton Dickinson 6-well tissue culture plate
(A) Autoencoder architecture used to integrate 3 omics <t>of</t> <t>HCC</t> data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and <t>miRNA</t> features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.
6 Well Tissue Culture Plate, supplied by Becton Dickinson, 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/Custom+Tissue+Arrays/pmc03222791-63-25-29?v=Becton+Dickinson
Average 90 stars, based on 1 article reviews
6-well tissue culture plate - by Bioz Stars, 2026-08
90/100 stars
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msd ® mouse th1/th2 multi-array ® tissue culture kit  (Meso Scale Diagnostics LLC)
90
Meso Scale Diagnostics LLC msd ® mouse th1/th2 multi-array ® tissue culture kit
(A) Autoencoder architecture used to integrate 3 omics <t>of</t> <t>HCC</t> data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and <t>miRNA</t> features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.
Msd ® Mouse Th1/Th2 Multi Array ® Tissue Culture Kit, supplied by Meso Scale Diagnostics 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/Custom+Tissue+Arrays/pmc04534326-227-30-39?v=Meso+Scale+Diagnostics+LLC
Average 90 stars, based on 1 article reviews
msd ® mouse th1/th2 multi-array ® tissue culture kit - by Bioz Stars, 2026-08
90/100 stars
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tissue culture membrane  (Corning Life Sciences)
90
Corning Life Sciences tissue culture membrane
(A) Autoencoder architecture used to integrate 3 omics <t>of</t> <t>HCC</t> data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and <t>miRNA</t> features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.
Tissue Culture Membrane, supplied by Corning Life Sciences, 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/Custom+Tissue+Arrays/pm16630836-226-26-29?v=Corning+Life+Sciences
Average 90 stars, based on 1 article reviews
tissue culture membrane - by Bioz Stars, 2026-08
90/100 stars
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Image Search Results


Available gene expression assays predictive of prognosis in early-stage CRC.

Journal: Cancers

Article Title: Principles of Molecular Utility for CMS Classification in Colorectal Cancer Management

doi: 10.3390/cancers15102746

Figure Lengend Snippet: Available gene expression assays predictive of prognosis in early-stage CRC.

Article Snippet: ColoPrint colon cancer recurrence assay , Agendia, Inc. , 18-gene expression profile , Fresh tissue or fresh, frozen tissue , Agendia customized whole-genome high-density microarrays , Not FDA approved , [ , , ] .

Techniques: Gene Expression, Expressing, Formalin-fixed Paraffin-Embedded, Amplification, Microarray, Derivative Assay

(A) Autoencoder architecture used to integrate 3 omics of HCC data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and miRNA features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research

Article Title: Deep Learning based multi-omics integration robustly predicts survival in liver cancer

doi: 10.1158/1078-0432.CCR-17-0853

Figure Lengend Snippet: (A) Autoencoder architecture used to integrate 3 omics of HCC data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and miRNA features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.

Article Snippet: 166 pairs of HCC/matched noncancerous normal tissue samples were downloaded, with CapitalBio custom Human miRNA array data ( {"type":"entrez-geo","attrs":{"text":"GSE31384","term_id":"31384"}} GSE31384 ) ( 33 ).

Techniques: DNA Methylation Assay, Transformation Assay, Methylation

Performance of classifier for the five external confirmation cohorts.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research

Article Title: Deep Learning based multi-omics integration robustly predicts survival in liver cancer

doi: 10.1158/1078-0432.CCR-17-0853

Figure Lengend Snippet: Performance of classifier for the five external confirmation cohorts.

Article Snippet: 166 pairs of HCC/matched noncancerous normal tissue samples were downloaded, with CapitalBio custom Human miRNA array data ( {"type":"entrez-geo","attrs":{"text":"GSE31384","term_id":"31384"}} GSE31384 ) ( 33 ).

Techniques: Microarray, DNA Methylation Assay