cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2069712
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.2069722.v2
title: Star Classification System for Age Models

description: This is the supplementary information for paper Flantua, Blaauw and Hooghiemstra, 2016. Clim.Past 12, 387-414, 2016. <br><br>Here we present the outcomes of this paper in terms of recalibrated age models and their corresponding descriptions. Furthermore in the README file there are instructions and a test-folder on how to do age model running using Clam that will produce a star classification. <br>Please open first the README file for more details.
DOI: 10.6084/m9.figshare.2069719.v2
title: Semiparametric Spatial Autoregressive Models With Endogenous Regressors: With an Application to Crime Data

description: This study considers semiparametric spatial autoregressive models that allow for endogenous regressors, as well as the heterogenous effects of these regressors across spatial units. For the model estimation, we propose a semiparametric series generalized method of moments estimator. We establish that the proposed estimator is both consistent and asymptotically normal. As an empirical illustration, we apply the proposed model and method to Tokyo crime data to estimate how the existence of a neighborhood police substation (NPS) affects the household burglary rate. The results indicate that the presence of an NPS helps reduce household burglaries, and that the effects of some variables are heterogenous with respect to residential distribution patterns. Furthermore, we show that using a model that does not adjust for the endogeneity of NPS does not allow us to observe the significant relationship between NPS and the household burglary rate. Supplementary materials for this article is available online.
DOI: 10.6084/m9.figshare.2069713.v1
title: GSE6691

description: TBA.
DOI: 10.6084/m9.figshare.2069722.v3
title: Star Classification System for Age Models

description: This is the supplementary information for paper Flantua, Blaauw and Hooghiemstra, 2016. Clim.Past 12, 387-414, 2016. <br><br>Here we present the outcomes of this paper in terms of recalibrated age models and their corresponding descriptions. Furthermore in the README file there are instructions and a test-folder on how to do age model running using Clam that will produce a star classification. <br>Please open first the README file for more details.
DOI: 10.6084/m9.figshare.2069710
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/).
DOI: 10.6084/m9.figshare.2069714.v1
title: GSE56369

description: TBA.<br>
DOI: 10.6084/m9.figshare.2069712.v1
title: GSE46449

description: TBA.
DOI: 10.6084/m9.figshare.2069713.v2
title: GSE46691

description: TBA.
DOI: 10.6084/m9.figshare.2069709
title: Biomarker Benchmark - GSE39491

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>"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.2069714.v2
title: GSE56369

description: TBA.<br>
DOI: 10.6084/m9.figshare.2069710.v1
title: GSE40292

description: TBA.
DOI: 10.6084/m9.figshare.2069712.v2
title: 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
DOI: 10.6084/m9.figshare.2069713.v3
title: GSE46691

description: TBA.
DOI: 10.6084/m9.figshare.2069714.v3
title: Gene expression data from Gene Expression Omnibus - GSE56396

description: "Evaluation of the airway transcriptome may reveal patterns of gene expression that are associated with clinical phenotypes of asthma. To define transcriptomic endotypes of asthma (TEA) we analyzed gene expression in induced sputum that correlate with phenotypes of disease. Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes identified TEA clusters. Clinical characteristics were compared."<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56396<br><br>
DOI: 10.6084/m9.figshare.2069710.v2
title: GSE40292

description: TBA.
DOI: 10.6084/m9.figshare.2069712.v3
title: 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
DOI: 10.6084/m9.figshare.2069709.v1
title: GSE39491

description: TBA.
DOI: 10.6084/m9.figshare.2069713.v4
title: Gene expression data from Gene Expression Omnibus - GSE46691

description: Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.<br><br>Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.<br><br>Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.<br><br>Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE46691
DOI: 10.6084/m9.figshare.2069709.v2
title: GSE39491

description: TBA.
DOI: 10.6084/m9.figshare.2069712.v4
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.2069710.v3
title: 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
DOI: 10.6084/m9.figshare.2069710.v4
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/).