cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2069698.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE19804

description: "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.2069696.v2
title: GSE15296

description: TBA.
DOI: 10.6084/m9.figshare.2069692
title: Biomarker Benchmark - GSE4271

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>"Novel prognostic subclasses of high-grade astrocytoma are identified and discovered to resemble stages in neurogenesis. One tumor class displaying neuronal lineage markers shows longer survival, while two tumor classes enriched for neural stem cell markers display equally short survival. Poor prognosis subclasses exhibit either markers of proliferation or of angiogenesis and mesenchyme. Analysis of gene expression data is described in Phillips et al., Cancer Cell, 2006.<br>Keywords: Disease state comparison"<br> <br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4271<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.2069698.v6
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.2069695.v2
title: GSE5460

description: TBA.
DOI: 10.6084/m9.figshare.2069698.v5
title: Biomarker Benchmark - GSE19804

description: "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.2069690
title: Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies

description: We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer quadratic program. Through simulation studies and data examples, we demonstrate that our formulation allows these problems to be solved in an expedient manner even for large datasets and for large strata. We further exhibit that through our formulation, one can assess the impact of various assumptions about the potential outcomes on the performed inference. R scripts are provided that implement our methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.2069695.v3
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE5460

description: "Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion.Keywords: disease state analysis"<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5460<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.2069696.v3
title: GSE15296

description: TBA.
DOI: 10.6084/m9.figshare.2069692.v1
title: GSE4271

description: TBA.
DOI: 10.6084/m9.figshare.2069695.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE5460

description: "Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion.Keywords: disease state analysis"<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5460<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.2069689
title: Modeling E-mail Networks and Inferring Leadership Using Self-Exciting Point Processes

description: We propose various self-exciting point process models for the times when e-mails are sent between individuals in a social network. Using an expectation–maximization (EM)-type approach, we fit these models to an e-mail network dataset from West Point Military Academy and the Enron e-mail dataset. We argue that the self-exciting models adequately capture major temporal clustering features in the data and perform better than traditional stationary Poisson models. We also investigate how accounting for diurnal and weekly trends in e-mail activity improves the overall fit to the observed network data. A motivation and application for fitting these self-exciting models is to use parameter estimates to characterize important e-mail communication behaviors such as the baseline sending rates, average reply rates, and average response times. A primary goal is to use these features, estimated from the self-exciting models, to infer the underlying leadership status of users in the West Point and Enron networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.2069690.v1
title: Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses with Binary Outcomes in Matched Observational Studies

description: We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer quadratic program. Through simulation studies and data examples, we demonstrate that our formulation allows these problems to be solved in an expedient manner even for large data sets and for large strata. We further exhibit that through our formulation, one can assess the impact of various assumptions about the potential outcomes on the performed inference. R scripts are provided that implement our methods.
DOI: 10.6084/m9.figshare.2069696.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE15296

description: <br>"In the present work, we have used whole genome expression profiling of peripheral blood samples from 51 patients with biopsy-proven acute kidney transplant rejection and 24 patients with excellent function and biopsy-proven normal transplant histology. The results demonstrate that there are 1738 probesets on the Affymetrix HG-U133 Plus 2.0 GeneChip representing 1472 unique genes which are differentially expressed in the peripheral blood during an acute kidney transplant rejection. By ranking these results we have identified minimal sets of 50 to 150 probesets with predictive classification accuracies for AR of greater than 90% established with several different prediction tools including DLDA and PAM. We have demonstrated that a subset of peripheral blood gene expression signatures can also diagnose four different subtypes of AR (Banff Borderline, IA, IB and IIA) and the top 100 ranked classifiers have greater than 89% predictive accuracy. Finally, we have demonstrated that there are gene signatures for early and late AR defined as less than or greater than one year post-transplant with greater than 86% predictive accuracies. We also confirmed that there are 439 time-independent gene classifiers for AR. Based on these results, we conclude that peripheral blood gene expression profiling can be used to diagnose AR at any time in the first 5 years post-transplant in the setting of acute kidney transplant dysfunction not caused by BK nephropathy, other infections, drug-induced nephrotoxicity or ureteral obstruction.<br><br>Keywords: kidney transplantation, peripheral blood, DNA microarrays, acute kidney rejection, biomarkers"<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15296<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.2069689.v1
title: Modeling E-mail Networks and Inferring Leadership Using Self-Exciting Point Processes

description: We propose various self-exciting point process models for the times when e-mails are sent between individuals in a social network. Using an expectation–maximization (EM)-type approach, we fit these models to an e-mail network dataset from West Point Military Academy and the Enron e-mail dataset. We argue that the self-exciting models adequately capture major temporal clustering features in the data and perform better than traditional stationary Poisson models. We also investigate how accounting for diurnal and weekly trends in e-mail activity improves the overall fit to the observed network data. A motivation and application for fitting these self-exciting models is to use parameter estimates to characterize important e-mail communication behaviors such as the baseline sending rates, average reply rates, and average response times. A primary goal is to use these features, estimated from the self-exciting models, to infer the underlying leadership status of users in the West Point and Enron networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.2069690.v3
title: Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies

description: We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer quadratic program. Through simulation studies and data examples, we demonstrate that our formulation allows these problems to be solved in an expedient manner even for large datasets and for large strata. We further exhibit that through our formulation, one can assess the impact of various assumptions about the potential outcomes on the performed inference. R scripts are provided that implement our methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.2069692.v2
title: GSE4271

description: 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.2069690.v2
title: Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies

description: We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer quadratic program. Through simulation studies and data examples, we demonstrate that our formulation allows these problems to be solved in an expedient manner even for large datasets and for large strata. We further exhibit that through our formulation, one can assess the impact of various assumptions about the potential outcomes on the performed inference. R scripts are provided that implement our methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.2069696.v6
title: Biomarker Benchmark - GSE15296

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>"In the present work, we have used whole genome expression profiling of peripheral blood samples from 51 patients with biopsy-proven acute kidney transplant rejection and 24 patients with excellent function and biopsy-proven normal transplant histology. The results demonstrate that there are 1738 probesets on the Affymetrix HG-U133 Plus 2.0 GeneChip representing 1472 unique genes which are differentially expressed in the peripheral blood during an acute kidney transplant rejection. By ranking these results we have identified minimal sets of 50 to 150 probesets with predictive classification accuracies for AR of greater than 90% established with several different prediction tools including DLDA and PAM. We have demonstrated that a subset of peripheral blood gene expression signatures can also diagnose four different subtypes of AR (Banff Borderline, IA, IB and IIA) and the top 100 ranked classifiers have greater than 89% predictive accuracy. Finally, we have demonstrated that there are gene signatures for early and late AR defined as less than or greater than one year post-transplant with greater than 86% predictive accuracies. We also confirmed that there are 439 time-independent gene classifiers for AR. Based on these results, we conclude that peripheral blood gene expression profiling can be used to diagnose AR at any time in the first 5 years post-transplant in the setting of acute kidney transplant dysfunction not caused by BK nephropathy, other infections, drug-induced nephrotoxicity or ureteral obstruction.<br><br>Keywords: kidney transplantation, peripheral blood, DNA microarrays, acute kidney rejection, biomarkers"<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15296<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/).