BARRA:CuRDa

BARRA:CuRDa

BARRA:CuRDa

A Curated RNA-seq Database for Cancer Research
Cite Workflow Datasets

RNA-seq is gradually becoming the dominating technique employed to access the global gene expression in biological samples, allowing more flexible protocols and robust analysis. However, the nature of RNA-seq results im-pose new data-handling challenges when it comes to computational analysis. With the increasing employment of machine learning techniques in biomedical sciences, databases that could provide curated datasets treated with state-of-the-art approaches already adapted to machine learning protocols become essential for testing new algorithms.

BARRA:CuRDa is composed of 17 handpicked RNA-seq datasets for Homo sapiens gathered from the Gene Expression Omnibus (GEO), using rigorous filtering criteria. All datasets were individually submitted to sample quality analysis, removal of low-quality bases, artifacts from the experimental process, removal of ribosomal RNA, and transcript level abundance. Moreover, like its sister database, all datasets were tested using analyses destined to provide a base knowledge of each dataset's characteristics, with the addition of new metrics.

How to CIte

If you use BARRA:CuRDa in a scientific publication, we would appreciate citations to the following paper:

BibTeX

@article{feltes:2021,
    author = {Feltes, Bruno Cesar and Poloni, Joice De Faria and Dorn, Marcio},
    doi = {10.1089/cmb.2020.0463},
    journal = {Journal of Computational Biology},
    number = {9},
    pages = {931--944},
    title = {{Benchmarking and Testing Machine Learning Approaches with BARRA:CuRDa, a Curated RNA-Seq Database for Cancer Research}},
    url = {https://doi.org/10.1089/cmb.2020.0463},
    volume = {28},
    year = {2021}
}

Workflow

Datasets

Tissue
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Tissue Prostate GSE GSE22260 Samples 28 Genes 58735 Classes 2
dt 0.271 rf 0.633 knn 0.643 mlp 0.526 svm 0.818
Tissue Lung GSE GSE37764 Samples 12 Genes 58735 Classes 2
dt 0.774 rf 0.979 knn 1 mlp 0.47 svm 0
Tissue Lung GSE GSE40419 Samples 164 Genes 58735 Classes 2
dt 0.943 rf 0.978 knn 0.939 mlp 0.518 svm 0.97
Tissue Head/Neck GSE GSE48850 Samples 11 Genes 58735 Classes 2
dt 0.689 rf 0.812 knn 0.727 mlp 0.478 svm 0.679
Tissue Colon GSE GSE50760 Samples 54 Genes 58735 Classes 3
dt 0.873 rf 0.805 knn 0.833 mlp 0.355 svm 0.093
Tissue Breast GSE GSE52194 Samples 20 Genes 58735 Classes 2
dt 0.918 rf 0.984 knn 0.85 mlp 0.756 svm 0.85
Tissue Liver GSE GSE55758 Samples 16 Genes 58735 Classes 2
dt 0.688 rf 0.816 knn 0.812 mlp 0.431 svm 0
Tissue Lung GSE GSE60052 Samples 86 Genes 58735 Classes 2
dt 0.967 rf 0.947 knn 0.942 mlp 0.881 svm 0.919
Tissue Head/Neck GSE GSE63511 Samples 16 Genes 58735 Classes 2
dt 0.827 rf 0.936 knn 0.875 mlp 0.456 svm 0.364
Tissue Head/Neck GSE GSE64912 Samples 22 Genes 58735 Classes 2
dt 0.833 rf 0.9 knn 0.864 mlp 0.648 svm 0
Tissue Head/Neck GSE GSE68799 Samples 45 Genes 58735 Classes 2
dt 0.948 rf 0.951 knn 1 mlp 0.875 svm 0.911
Tissue Breast GSE GSE69240 Samples 35 Genes 58735 Classes 2
dt 0.938 rf 1 knn 1 mlp 0.619 svm 0.914
Tissue Breast GSE GSE71651 Samples 33 Genes 58735 Classes 2
dt 0.549 rf 0.457 knn 0.576 mlp 0.559 svm 0.545
Tissue Colon GSE GSE72820 Samples 14 Genes 58735 Classes 2
dt 0.53 rf 0.304 knn 0.357 mlp 0.456 svm 0
Tissue Lung GSE GSE87340 Samples 51 Genes 58735 Classes 2
dt 0.929 rf 0.98 knn 0.941 mlp 0.491 svm 0.588
Tissue Kidney GSE GSE89122 Samples 13 Genes 58735 Classes 2
dt 0.444 rf 0.422 knn 0.462 mlp 0.528 svm 0.077
Tissue Colon GSE SRR2089755 Samples 20 Genes 58148 Classes 4
dt 0.65 rf 0.824 knn 1 mlp 0.268 svm 0
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