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dna microarray gene expression datasets  (Biotechnology Information)

 
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    Biotechnology Information dna microarray gene expression datasets
    Workflow of this study. The <t>DNA</t> <t>microarray</t> gene expression datasets of 92 borderline ovarian tumor (BOT) samples and 136 normal ovarian controls were downloaded from a publicly available database with the gene set regularity (GSR) index calculated by the Gene Ontology (GO) gene set. Functionomes consisting of 10,192 GO gene sets established from the polygenic model and machine learning with statistical methods and cumulative portion transformations were used to recognize the functionome patterns to discover dysfunctional GO terms, pathways, and differentially expressed genes (DEGs). Finally, the pathogenesis of BOTs was investigated by integrative analysis.
    Dna Microarray Gene Expression Datasets, supplied by Biotechnology Information, 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/dna microarray gene expression datasets/product/Biotechnology Information
    Average 90 stars, based on 1 article reviews
    dna microarray gene expression datasets - by Bioz Stars, 2026-03
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    1) Product Images from "Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics"

    Article Title: Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics

    Journal: International Journal of Molecular Sciences

    doi: 10.3390/ijms22084105

    Workflow of this study. The DNA microarray gene expression datasets of 92 borderline ovarian tumor (BOT) samples and 136 normal ovarian controls were downloaded from a publicly available database with the gene set regularity (GSR) index calculated by the Gene Ontology (GO) gene set. Functionomes consisting of 10,192 GO gene sets established from the polygenic model and machine learning with statistical methods and cumulative portion transformations were used to recognize the functionome patterns to discover dysfunctional GO terms, pathways, and differentially expressed genes (DEGs). Finally, the pathogenesis of BOTs was investigated by integrative analysis.
    Figure Legend Snippet: Workflow of this study. The DNA microarray gene expression datasets of 92 borderline ovarian tumor (BOT) samples and 136 normal ovarian controls were downloaded from a publicly available database with the gene set regularity (GSR) index calculated by the Gene Ontology (GO) gene set. Functionomes consisting of 10,192 GO gene sets established from the polygenic model and machine learning with statistical methods and cumulative portion transformations were used to recognize the functionome patterns to discover dysfunctional GO terms, pathways, and differentially expressed genes (DEGs). Finally, the pathogenesis of BOTs was investigated by integrative analysis.

    Techniques Used: Microarray, Gene Expression



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    dna microarray gene expression datasets  (Biotechnology Information)
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    Biotechnology Information dna microarray gene expression datasets
    Workflow of this study. The <t>DNA</t> <t>microarray</t> gene expression datasets of 92 borderline ovarian tumor (BOT) samples and 136 normal ovarian controls were downloaded from a publicly available database with the gene set regularity (GSR) index calculated by the Gene Ontology (GO) gene set. Functionomes consisting of 10,192 GO gene sets established from the polygenic model and machine learning with statistical methods and cumulative portion transformations were used to recognize the functionome patterns to discover dysfunctional GO terms, pathways, and differentially expressed genes (DEGs). Finally, the pathogenesis of BOTs was investigated by integrative analysis.
    Dna Microarray Gene Expression Datasets, supplied by Biotechnology Information, 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/dna microarray gene expression datasets/product/Biotechnology Information
    Average 90 stars, based on 1 article reviews
    dna microarray gene expression datasets - by Bioz Stars, 2026-03
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    moc dna microarray gene expression datasets  (Biotechnology Information)
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    Workflow of this study. The <t>DNA</t> <t>microarray</t> gene expression datasets of 92 borderline ovarian tumor (BOT) samples and 136 normal ovarian controls were downloaded from a publicly available database with the gene set regularity (GSR) index calculated by the Gene Ontology (GO) gene set. Functionomes consisting of 10,192 GO gene sets established from the polygenic model and machine learning with statistical methods and cumulative portion transformations were used to recognize the functionome patterns to discover dysfunctional GO terms, pathways, and differentially expressed genes (DEGs). Finally, the pathogenesis of BOTs was investigated by integrative analysis.
    Moc Dna Microarray Gene Expression Datasets, supplied by Biotechnology Information, 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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    gene expression dna microarray dataset  (Almac Inc)
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    Almac Inc gene expression dna microarray dataset
    A: Supervised hierarchical clustering displaying the genes significantly modulated between the resistant and sensitive cell lines (52 under‐ and 51 over‐expressed in resistant cell lines) using a cutoff of twofold change on expression and FDR < 0.01. B: Fold difference in gene expression between sensitive and resistant cell lines detected by qRT‐PCR analyses. Seven genes were selected from the list of altered genes for this analysis. The results from sensitive or resistant cell lines were pooled to obtain an estimated fold change. C: Correlation between the qRT‐PCR and the <t>microarray</t> expression data. The fold difference detected by qRT‐PCR between sensitive and resistant cell lines correlated significantly confirming the microarray results (Pearson's correlation or ρ = 0.739, p ‐value = 0.013).
    Gene Expression Dna Microarray Dataset, supplied by Almac 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/gene expression dna microarray dataset/product/Almac Inc
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    Workflow of this study. The DNA microarray gene expression datasets of 92 borderline ovarian tumor (BOT) samples and 136 normal ovarian controls were downloaded from a publicly available database with the gene set regularity (GSR) index calculated by the Gene Ontology (GO) gene set. Functionomes consisting of 10,192 GO gene sets established from the polygenic model and machine learning with statistical methods and cumulative portion transformations were used to recognize the functionome patterns to discover dysfunctional GO terms, pathways, and differentially expressed genes (DEGs). Finally, the pathogenesis of BOTs was investigated by integrative analysis.

    Journal: International Journal of Molecular Sciences

    Article Title: Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics

    doi: 10.3390/ijms22084105

    Figure Lengend Snippet: Workflow of this study. The DNA microarray gene expression datasets of 92 borderline ovarian tumor (BOT) samples and 136 normal ovarian controls were downloaded from a publicly available database with the gene set regularity (GSR) index calculated by the Gene Ontology (GO) gene set. Functionomes consisting of 10,192 GO gene sets established from the polygenic model and machine learning with statistical methods and cumulative portion transformations were used to recognize the functionome patterns to discover dysfunctional GO terms, pathways, and differentially expressed genes (DEGs). Finally, the pathogenesis of BOTs was investigated by integrative analysis.

    Article Snippet: DNA microarray gene expression datasets were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, and the sample data were obtained from 28 dataset series containing six different DNA microarray platforms without any missing data.

    Techniques: Microarray, Gene Expression

    A: Supervised hierarchical clustering displaying the genes significantly modulated between the resistant and sensitive cell lines (52 under‐ and 51 over‐expressed in resistant cell lines) using a cutoff of twofold change on expression and FDR < 0.01. B: Fold difference in gene expression between sensitive and resistant cell lines detected by qRT‐PCR analyses. Seven genes were selected from the list of altered genes for this analysis. The results from sensitive or resistant cell lines were pooled to obtain an estimated fold change. C: Correlation between the qRT‐PCR and the microarray expression data. The fold difference detected by qRT‐PCR between sensitive and resistant cell lines correlated significantly confirming the microarray results (Pearson's correlation or ρ = 0.739, p ‐value = 0.013).

    Journal: The Journal of Pathology: Clinical Research

    Article Title: A 12‐gene signature to distinguish colon cancer patients with better clinical outcome following treatment with 5‐fluorouracil or FOLFIRI

    doi: 10.1002/cjp2.17

    Figure Lengend Snippet: A: Supervised hierarchical clustering displaying the genes significantly modulated between the resistant and sensitive cell lines (52 under‐ and 51 over‐expressed in resistant cell lines) using a cutoff of twofold change on expression and FDR < 0.01. B: Fold difference in gene expression between sensitive and resistant cell lines detected by qRT‐PCR analyses. Seven genes were selected from the list of altered genes for this analysis. The results from sensitive or resistant cell lines were pooled to obtain an estimated fold change. C: Correlation between the qRT‐PCR and the microarray expression data. The fold difference detected by qRT‐PCR between sensitive and resistant cell lines correlated significantly confirming the microarray results (Pearson's correlation or ρ = 0.739, p ‐value = 0.013).

    Article Snippet: Hence, we used a publically available gene expression DNA microarray dataset generated by Almac Diagnostics on 359 FFPE tissue specimens from stage II colon cancer that did not receive adjuvant chemotherapy.

    Techniques: Expressing, Gene Expression, Quantitative RT-PCR, Microarray