cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2069712.v6
title: Biomarker Benchmark - GSE46449

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><br>"There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated bipolar patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD.<br>We employed microarray technology to measure global leukocyte gene expression in first-episode (n=3) and currently medicated BPD patients (n=26), and matched healthy controls (n=25). Following an initial feature selection of the microarray data, we developed a cross-validated 10-gene model that was able to correctly predict the diagnostic group of the training sample (26 medicated patients and 12 controls), with 89% sensitivity and 75% specificity (p&lt;0.001). The 10-gene predictor was further explored via testing on an independent test cohort consisting of three pairs of monozygotic twins discordant for BPD, plus the original enrichment sample cohort (the three never-medicated BPD patients and 13 matched control subjects), and a sample of experimental replicates (n=34). 83% of the independent test sample was correctly predicted, with a sensitivity of 67% and specificity of 100% (although this result did not reach statistical significance). Additionally, 88% of sample diagnostic classes were classified correctly for both the enrichment (p=0.015) and the replicate samples (p&lt;0.001)."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE46449<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.2069710.v5
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE40292

description: "Genome-wide association studies (GWAS) have been pivotal to increasing our understanding of intestinal disease. However, the mode by which genetic variation results in phenotypic change remains largely unknown, with many associated polymorphisms likely to modulate gene expression. Analyses of expression quantitative trait loci (eQTL) to date indicate that as many as 50% of these are tissue specific. Here we report a comprehensive eQTL scan of intestinal tissue."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40292<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.2069714.v5
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE30784

description: <br>"OSCC is associated with substantial mortality and morbidity. To identify potential biomarkers for the early detection of invasive OSCC, we compared the gene expressions of OSCC, oral dysplasia, and normal<br>oral tissue from patients without oral cancer or preneoplastic oral lesions (controls). Results provided models of gene expression to distinguish OSCC from controls."<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30784<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/).
DOI: 10.6084/m9.figshare.2069709.v3
title: Gene expression data from Gene Expression Omnibus - GSE39491

description: "Barrett’s esophagus (BE) is a metaplastic precursor lesion of esophageal adenocarcinoma (EA), the most rapidly increasing cancer in western societies. While the prevalence of BE is increasing, the vast majority of EA occurs in patients with undiagnosed BE. Thus, we sought to identify genes that are altered in BE compared to the normal mucosa of the esophagus, and which may be potential biomarkers for the development or diagnosis of BE."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39491
DOI: 10.6084/m9.figshare.2069712.v5
title: Biomarker Benchmark - Gene exression data from Gene Expression Omnibus - GSE46449

description: "There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated bipolar patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD.<br>We employed microarray technology to measure global leukocyte gene expression in first-episode (n=3) and currently medicated BPD patients (n=26), and matched healthy controls (n=25). Following an initial feature selection of the microarray data, we developed a cross-validated 10-gene model that was able to correctly predict the diagnostic group of the training sample (26 medicated patients and 12 controls), with 89% sensitivity and 75% specificity (p&lt;0.001). The 10-gene predictor was further explored via testing on an independent test cohort consisting of three pairs of monozygotic twins discordant for BPD, plus the original enrichment sample cohort (the three never-medicated BPD patients and 13 matched control subjects), and a sample of experimental replicates (n=34). 83% of the independent test sample was correctly predicted, with a sensitivity of 67% and specificity of 100% (although this result did not reach statistical significance). Additionally, 88% of sample diagnostic classes were classified correctly for both the enrichment (p=0.015) and the replicate samples (p&lt;0.001)."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE46449<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.2069714.v7
title: Biomarker Benchmark - GSE30784

description: <br><b>[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><br>"OSCC is associated with substantial mortality and morbidity. To identify potential biomarkers for the early detection of invasive OSCC, we compared the gene expressions of OSCC, oral dysplasia, and normal<br>oral tissue from patients without oral cancer or preneoplastic oral lesions (controls). Results provided models of gene expression to distinguish OSCC from controls."<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30784<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/).
DOI: 10.6084/m9.figshare.2069707
title: Biomarker Benchmark - GSE37745

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><b><br></b>" Background: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multi-gene signatures in clinical practice is unclear and the biological importance of individual genes is difficult to assess as the published signatures virtually do not overlap.<br><br>Methods: Here we describe a novel single institute cohort, including 196 non-small lung cancer (NSCLC) cases with clinical information and long-term follow-up, which was used as a training set to screen for single genes with prognostic impact. The top 450 gene probe sets identified using a univariate Cox regression model (significance level p&lt;0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n=860).<br><br>Results: The meta-analysis revealed that 17 probe sets were significantly associated with survival (p&lt;0.0005) with a false discovery rate of 1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on a tissue microarray including 355 NSCLC samples. Low CADM1 protein expression was associated with shorter survival (p=0.028), with particular influence in the adenocarcinoma patient subgroup (p=0.002).<br><br>Conclusions: We were able to validate single genes with independent prognostic impact using a novel NSCLC cohort together with a meta-analysis approach. CADM1 was identified as an immunohistochemical marker with a potential application in clinical diagnostics."<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37745<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/).
DOI: 10.6084/m9.figshare.2069708
title: Biomarker Benchmark - GSE38958

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>"Idiopathic pulmonary fibrosis (IPF) is a specific form of chronic, progressive fibrosing interstitial disease of unknown cause. It remains impractical to conduct early diagnosis and predict IPF progression just based on gene expression information. Moreover, the relationship between gene expression and quantitative phenotypic value in IPF keeps controversial. To identify biomarkers to predict survival in IPF, we profiled protein-coding gene expression in peripheral blood mononuclear cells (PBMCs). We linked the gene expression level with the quantitative phenotypic variation in IPF, including diffusing capacity of the lung for carbon monoxide (DLCO) and forced vital capacity (FVC) percent predicted. In silico analyses on the expression profiles and quantitative phenotypic data allowed for the generation of a set of IPF molecular signature that predicted survival of IPF effectively."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38958<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.2069710.v6
title: Biomarker Benchmark - GSE40292

description: "Genome-wide association studies (GWAS) have been pivotal to increasing our understanding of intestinal disease. However, the mode by which genetic variation results in phenotypic change remains largely unknown, with many associated polymorphisms likely to modulate gene expression. Analyses of expression quantitative trait loci (eQTL) to date indicate that as many as 50% of these are tissue specific. Here we report a comprehensive eQTL scan of intestinal tissue."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40292<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.2069714.v6
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE30784

description: "OSCC is associated with substantial mortality and morbidity. To identify potential biomarkers for the early detection of invasive OSCC, we compared the gene expressions of OSCC, oral dysplasia, and normal<br>oral tissue from patients without oral cancer or preneoplastic oral lesions (controls). Results provided models of gene expression to distinguish OSCC from controls."<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30784<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/).
DOI: 10.6084/m9.figshare.2069709.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE39491

description: "Barrett’s esophagus (BE) is a metaplastic precursor lesion of esophageal adenocarcinoma (EA), the most rapidly increasing cancer in western societies. While the prevalence of BE is increasing, the vast majority of EA occurs in patients with undiagnosed BE. Thus, we sought to identify genes that are altered in BE compared to the normal mucosa of the esophagus, and which may be potential biomarkers for the development or diagnosis of BE."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39491<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.2069710.v8
title: Biomarker Benchmark - GSE40292

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>"Genome-wide association studies (GWAS) have been pivotal to increasing our understanding of intestinal disease. However, the mode by which genetic variation results in phenotypic change remains largely unknown, with many associated polymorphisms likely to modulate gene expression. Analyses of expression quantitative trait loci (eQTL) to date indicate that as many as 50% of these are tissue specific. Here we report a comprehensive eQTL scan of intestinal tissue."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40292<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/).