cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2069705.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE37147

description: "RNA was isolated from bronchial brushings obtained from current and former smokers with and without COPD. mRNA expression was profiled using Affymetrix Human Gene 1.0 ST Arrays."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37147<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069703.v1
title: GSE31048

description: TBA.
DOI: 10.6084/m9.figshare.2069704.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE32646

description: "The purpose of the present study was to investigate the association of glutathione S-transferase P1 (GSTP1) expression with resistance to neoadjuvant paclitaxel followed by 5-fluorouracil/epirubicin/cyclophosphamide (P-FEC) in human breast cancers. The relationship of GSTP1 expression and GSTP1 promoter hypermethylation with intrinsic subtypes was also investigated. In this study, primary breast cancer patients (n = 123, stage II-III) treated with neoadjuvant P-FEC were analyzed. Tumor samples were obtained by vacuum-assisted core biopsy before P-FEC. GSTP1 expression was determined using immunohistochemistry, GSTP1 promoter methylation index (MI) using bisulfite methylation assay and intrinsic subtypes using DNA microarray. The pathological complete response (pCR) rate was significantly higher in GSTP1-negative tumors (80.0%) than GSTP1-positive tumors (30.6%) (P = 0.009) among estrogen receptor (ER)-negative tumors but not among ER-positive tumors (P = 0.267). Multivariate analysis showed that GSTP1 was the only predictive factor for pCR (P = 0.013) among ER-negative tumors. Luminal A, luminal B and HER2-enriched tumors showed a significantly lower GSTP1 positivity than basal-like tumors (P = 0.002, P &lt; 0.001 and P = 0.009, respectively), while luminal A, luminal B and HER2-enriched tumors showed a higher GSTP1 MI than basal-like tumors (P = 0.076, P &lt; 0.001 and P &lt; 0.001, respectively). In conclusion, these results suggest the possibility that GSTP1 expression can predict pathological response to P-FEC in ER-negative tumors but not in ER-positive tumors. Additionally, GSTP1 promoter hypermethylation might be implicated more importantly in the pathogenesis of luminal A, luminal B and HER2-enriched tumors than basal-like tumors."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32646<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069700
title: Biomarker Benchmark - GSE21510

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"Purpose: This study aimed to identify a novel biomarker or a target of treatment for colorectal cancer (CRC).Experimental Design: The expression profiles of cancer cells in 104 patients with CRC were examined using laser microdissection and oligonucleotide microarray analysis. Overexpression in CRC cells especially in patients with distant metastasis was a prerequisite to select candidate genes. The mRNA expression of candidate gene was investigated by quantitative reversetranscription polymerase chain reaction (RT-PCR) in 77 patients as a validation study. We analyzed the protein expression and localization of the candidate gene by immunohistochemical study, and investigated the relationship between the expression and the clinicopathological feature in 274 CRC patients.<br>Results: We identified 6 genes as candidates related to distant metastasis in CRC patients by microarray analysis. Among these genes, Osteoprotegerin (OPG) is known to have an association with aggressiveness in several cancers through inhibiting apoptosis by neutralizing the function of tumor necrosis factor related apoptosis inducing ligand (TRAIL). The mRNA expression of OPG in cancer tissues was significantly higher in patients with distant metastasis than those without metastasis. The overexpression of OPG protein was significantly associated with worse overall survival and relapse free survival (RFS). Moreover, the overexpression of OPG protein was an independent risk factor for recurrence of CRC.<br>Conclusion: The overexpression of OPG would be a predictive biomarker of recurrence and a target of the treatment for CRC."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21510<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).
DOI: 10.6084/m9.figshare.2069706.v5
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE37199

description: "Background: Inter-patient prostate cancer (PrCa) heterogeneity results in highly variable patient outcomes. Multi-purpose biomarkers to dissect this heterogeneity are urgently required to improve treatment and accelerate drug development in PrCa. Circulating biomarkers are most practical for evaluating this disease. We pursued the analytical validation and clinical qualification of blood mRNA expression arrays.<br>Methods: Whole blood samples were collected into PaxGeneTM tubes from PrCa patients: 31 good prognosis patients selected for active surveillance (AS) and 63 advanced castration resistant PrCa (CRPC) patients. RNA was extracted, amplified, biotinylated, and hybridised to Affymetrix U133plus2 microarrays and analysis of genome-wide expression profiles were analysed using Bayesian Latent Process Decomposition (LPD).<br>Findings: LPD analysis of the mRNA expression data divided the evaluable patients (n=94) into 4 separate groups. LPD1 and LPD2 consisted almost entirely of CRPC patients (14/14; 17/18); LPD3 (15/31) and LDP4 (12/21) comprised both AS and CRPC. Ten patients were unclassifiable by LPD analysis. LPD1 CRPC patients had significantly poorer overall survival (median 10.7 months, CI-95% 4.2-17.2) than CRPC patients in LPD2 to 4 (median 26.5 months, CI-95% 18.1-34.9, p=0.00007). LPD 1 membership remained the strongest prognostic factor in a multivariate analysis (HR 5.0, CI-95% 2.1-11.9, p=0.0002). Gene signatures in the poor prognosis LPD group 1 were associated with increased CD71+ early erythroid cells and decreased B-cell and T-cell immune response. A 9-gene signature, that was validated by RT-PCR studies, classified patients as group LPD 1 with a very low misclassification rate (1.2%).<br>Interpretation: Poor PrCa outcome can be predicted using gene expression signatures from whole blood and merits further evaluation in predictive and pharmacodynamic biomarker studies for novel anticancer drugs including immune therapies"<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37199<br><br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br><br>
DOI: 10.6084/m9.figshare.2069703.v2
title: GSE31048

description: TBA.
DOI: 10.6084/m9.figshare.2069700.v1
title: GSE21510

description: TBA.<br>
DOI: 10.6084/m9.figshare.2069702.v1
title: GSE27854

description: TBA.
DOI: 10.6084/m9.figshare.2069706.v6
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE37199

description: "Background: Inter-patient prostate cancer (PrCa) heterogeneity results in highly variable patient outcomes. Multi-purpose biomarkers to dissect this heterogeneity are urgently required to improve treatment and accelerate drug development in PrCa. Circulating biomarkers are most practical for evaluating this disease. We pursued the analytical validation and clinical qualification of blood mRNA expression arrays.<br>Methods: Whole blood samples were collected into PaxGeneTM tubes from PrCa patients: 31 good prognosis patients selected for active surveillance (AS) and 63 advanced castration resistant PrCa (CRPC) patients. RNA was extracted, amplified, biotinylated, and hybridised to Affymetrix U133plus2 microarrays and analysis of genome-wide expression profiles were analysed using Bayesian Latent Process Decomposition (LPD).<br>Findings: LPD analysis of the mRNA expression data divided the evaluable patients (n=94) into 4 separate groups. LPD1 and LPD2 consisted almost entirely of CRPC patients (14/14; 17/18); LPD3 (15/31) and LDP4 (12/21) comprised both AS and CRPC. Ten patients were unclassifiable by LPD analysis. LPD1 CRPC patients had significantly poorer overall survival (median 10.7 months, CI-95% 4.2-17.2) than CRPC patients in LPD2 to 4 (median 26.5 months, CI-95% 18.1-34.9, p=0.00007). LPD 1 membership remained the strongest prognostic factor in a multivariate analysis (HR 5.0, CI-95% 2.1-11.9, p=0.0002). Gene signatures in the poor prognosis LPD group 1 were associated with increased CD71+ early erythroid cells and decreased B-cell and T-cell immune response. A 9-gene signature, that was validated by RT-PCR studies, classified patients as group LPD 1 with a very low misclassification rate (1.2%).<br>Interpretation: Poor PrCa outcome can be predicted using gene expression signatures from whole blood and merits further evaluation in predictive and pharmacodynamic biomarker studies for novel anticancer drugs including immune therapies"<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37199<br><br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br><br>
DOI: 10.6084/m9.figshare.2069698
title: Biomarker Benchmark - GSE19804

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"Although smoking is the major risk factor for lung cancer, only 7% of female lung cancer patients in Taiwan have a history of cigarette smoking, extremely lower than those in Caucasian females. This report is a comprehensive analysis of the molecular signature of non-smoking female lung cancer in Taiwan."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19804<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).
DOI: 10.6084/m9.figshare.2069700.v2
title: GSE21510

description: TBA.<br>
DOI: 10.6084/m9.figshare.2069703.v3
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE31048

description: "Wnt pathway is dysregulated in CLL-We characterized Wnt pathway gene expression in normal B and CLL-B cells and identified Wnt targets in normal B and CLL-B cells through this data set.In this dataset, we included normal B cells and CLL-B cells for Wnt pathway gene expression. This leads to the identification of 62 Wnt pathway components which are differnetially expressed between normal and CLl-B cells. We also included normal B cells and CLL-B cells with or without Wnt3a treatment and identified 468 and 676 Wnt regulated genes in normal and CLL B cells, respectively."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31048<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069701.v1
title: GSE25507

description: TBA.
DOI: 10.6084/m9.figshare.2069700.v3
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE21510

description: "Purpose: This study aimed to identify a novel biomarker or a target of treatment for colorectal cancer (CRC).Experimental Design: The expression profiles of cancer cells in 104 patients with CRC were examined using laser microdissection and oligonucleotide microarray analysis. Overexpression in CRC cells especially in patients with distant metastasis was a prerequisite to select candidate genes. The mRNA expression of candidate gene was investigated by quantitative reversetranscription polymerase chain reaction (RT-PCR) in 77 patients as a validation study. We analyzed the protein expression and localization of the candidate gene by immunohistochemical study, and investigated the relationship between the expression and the clinicopathological feature in 274 CRC patients.<br>Results: We identified 6 genes as candidates related to distant metastasis in CRC patients by microarray analysis. Among these genes, Osteoprotegerin (OPG) is known to have an association with aggressiveness in several cancers through inhibiting apoptosis by neutralizing the function of tumor necrosis factor related apoptosis inducing ligand (TRAIL). The mRNA expression of OPG in cancer tissues was significantly higher in patients with distant metastasis than those without metastasis. The overexpression of OPG protein was significantly associated with worse overall survival and relapse free survival (RFS). Moreover, the overexpression of OPG protein was an independent risk factor for recurrence of CRC.<br>Conclusion: The overexpression of OPG would be a predictive biomarker of recurrence and a target of the treatment for CRC."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21510<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).
DOI: 10.6084/m9.figshare.2069702.v2
title: GSE27854

description: TBA.
DOI: 10.6084/m9.figshare.2069703.v5
title: Biomarker Benchmark - GSE31048

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"Wnt pathway is dysregulated in CLL-We characterized Wnt pathway gene expression in normal B and CLL-B cells and identified Wnt targets in normal B and CLL-B cells through this data set.In this dataset, we included normal B cells and CLL-B cells for Wnt pathway gene expression. This leads to the identification of 62 Wnt pathway components which are differnetially expressed between normal and CLl-B cells. We also included normal B cells and CLL-B cells with or without Wnt3a treatment and identified 468 and 676 Wnt regulated genes in normal and CLL B cells, respectively."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31048<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069698.v1
title: GSE19804

description: TBA.
DOI: 10.6084/m9.figshare.2069703.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE31048

description: "Wnt pathway is dysregulated in CLL-We characterized Wnt pathway gene expression in normal B and CLL-B cells and identified Wnt targets in normal B and CLL-B cells through this data set.In this dataset, we included normal B cells and CLL-B cells for Wnt pathway gene expression. This leads to the identification of 62 Wnt pathway components which are differnetially expressed between normal and CLl-B cells. We also included normal B cells and CLL-B cells with or without Wnt3a treatment and identified 468 and 676 Wnt regulated genes in normal and CLL B cells, respectively."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31048<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069701.v2
title: GSE25507

description: TBA.