cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2069700.v4
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.v3
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE27854

description: "Purpose: The purpose of this study is to identify a novel biomarker related with distant metastases of colorectal cancer (CRC).Experimental Design: We investigated mRNA expression profiles in 115 patients with CRC using an Affymetrix Gene Chip, and copy number profiles in 122 patients with CRC using an Affymetrix DNA Sty array. Genes in common between copy number and expression data were extracted as candidate genes. We analyzed the mRNA expression of candidate gene by quantitative reverse transcription polymerase chain reaction (RT-PCR) in 86 patients as a validation study. Furthermore, we analyzed the protein expression of candidate gene by immunohistochemical study in 269 patients, and investigated the relationship between protein expression and clinicopathologic features.<br>Results: By the combination of copy number analysis and gene expression analysis, We extracted 2 candidate genes related with distant metastases of CRC. Several reports show that NUCKS1, one of candidate genes, is overexpressed in several cancer tissues. But a study about the relationship between NUCKS1 and CRC is none. The mRNA expression of NUCKS1 in cancer tissues was significantly higher than those in normal tissues. Overexpression of NUCKS1 protein was associated with significantly worse relapse-free survival of CRC. Overexpression of NUCKS1 protein was an independent risk factor for recurrence of CRC.<br>Conclusion: The overexpression of NUCKS1 would be a new biomarker predicting recurrence after colorectal surgery."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27854<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.2069697
title: neural-accuracy.h5

description: Data used to reproduce Figure 4 in the CogSci paper.<br>
DOI: 10.6084/m9.figshare.2069698.v2
title: GSE19804

description: TBA.
DOI: 10.6084/m9.figshare.2069700.v6
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.2069702.v5
title: Biomarker Benchmark - GSE27854

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: The purpose of this study is to identify a novel biomarker related with distant metastases of colorectal cancer (CRC).Experimental Design: We investigated mRNA expression profiles in 115 patients with CRC using an Affymetrix Gene Chip, and copy number profiles in 122 patients with CRC using an Affymetrix DNA Sty array. Genes in common between copy number and expression data were extracted as candidate genes. We analyzed the mRNA expression of candidate gene by quantitative reverse transcription polymerase chain reaction (RT-PCR) in 86 patients as a validation study. Furthermore, we analyzed the protein expression of candidate gene by immunohistochemical study in 269 patients, and investigated the relationship between protein expression and clinicopathologic features.<br>Results: By the combination of copy number analysis and gene expression analysis, We extracted 2 candidate genes related with distant metastases of CRC. Several reports show that NUCKS1, one of candidate genes, is overexpressed in several cancer tissues. But a study about the relationship between NUCKS1 and CRC is none. The mRNA expression of NUCKS1 in cancer tissues was significantly higher than those in normal tissues. Overexpression of NUCKS1 protein was associated with significantly worse relapse-free survival of CRC. Overexpression of NUCKS1 protein was an independent risk factor for recurrence of CRC.<br>Conclusion: The overexpression of NUCKS1 would be a new biomarker predicting recurrence after colorectal surgery."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27854<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.2069697.v1
title: neural-accuracy.h5

description: Data used to reproduce Figure 4 in the CogSci paper.<br>
DOI: 10.6084/m9.figshare.2069701.v3
title: GSE25507

description: TBA.
DOI: 10.6084/m9.figshare.2069696
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/).
DOI: 10.6084/m9.figshare.2069700.v5
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.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE27854

description: "Purpose: The purpose of this study is to identify a novel biomarker related with distant metastases of colorectal cancer (CRC).Experimental Design: We investigated mRNA expression profiles in 115 patients with CRC using an Affymetrix Gene Chip, and copy number profiles in 122 patients with CRC using an Affymetrix DNA Sty array. Genes in common between copy number and expression data were extracted as candidate genes. We analyzed the mRNA expression of candidate gene by quantitative reverse transcription polymerase chain reaction (RT-PCR) in 86 patients as a validation study. Furthermore, we analyzed the protein expression of candidate gene by immunohistochemical study in 269 patients, and investigated the relationship between protein expression and clinicopathologic features.<br>Results: By the combination of copy number analysis and gene expression analysis, We extracted 2 candidate genes related with distant metastases of CRC. Several reports show that NUCKS1, one of candidate genes, is overexpressed in several cancer tissues. But a study about the relationship between NUCKS1 and CRC is none. The mRNA expression of NUCKS1 in cancer tissues was significantly higher than those in normal tissues. Overexpression of NUCKS1 protein was associated with significantly worse relapse-free survival of CRC. Overexpression of NUCKS1 protein was an independent risk factor for recurrence of CRC.<br>Conclusion: The overexpression of NUCKS1 would be a new biomarker predicting recurrence after colorectal surgery."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27854<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
title: Biomarker Benchmark - GSE5460

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>"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.2069698.v3
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.v1
title: GSE15296

description: TBA.
DOI: 10.6084/m9.figshare.2069695.v1
title: GSE5460

description: TBA.