VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classification of Clinical and Genomic TCGA Data

Alba Garin-Muga; Alba Garin-Muga; Aurora María Sucre; Aurora María Sucre; Jordi Torres; Jon Kerexeta

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Paper

VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classification of Clinical and Genomic TCGA Data

Topics: Data Mining and Data Analysis; Data Visualization; Medical Informatics; Pattern Recognition and Machine Learning; Software Systems in Medicine

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: BIOSTEC, 413-420, 2020 , Valletta, Malta

Authors: Alba Garin-Muga ^{1

;

2} ; Aurora María Sucre ^{1

;

2} ; Jordi Torres ² and Jon Kerexeta ²

Affiliations: ¹ Biodonostia, Bioengineering Area, eHealth Group, Donostia-San Sebastián 20014, Spain ; ² Vicomtech, eHealth and Biomedical Applications Area, Donostia-San Sebastian 20014, Spain

Keyword(s): TCGA, Stratification, ML, Visualization, Clinical Data, Genomics

Abstract: The Cancer Genome Atlas (TCGA) is a collection of freely available data of several human cancer types. TCGA contains over 2.5 petabytes of data, which includes, among others, clinical and genomic data. However, the visualization of such data is cumbersome and tiring for non-expert users. VisualMLTCGA is an intuitive and easy-to-use web tool that allows the automatic download and visualization of TCGA data and the processing of genomic data using GATK. Additionally, the tool allows to create comprehensive decision trees (DT) for prediction of outcomes from clinical and genomic TCGA data and other external datasets. VisualMLTCGA offers a simple web tool to download, process and visualize TCGA data, suitable for researchers and clinicians without any bioinformatics background.

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Paper citation in several formats:

Garin-Muga, A.; Sucre, A.; Torres, J. and Kerexeta, J. (2020). VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classification of Clinical and Genomic TCGA Data. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 413-420. DOI: 10.5220/0008951804130420

@conference{healthinf20,
author={Alba Garin{-}Muga. and Aurora María Sucre. and Jordi Torres. and Jon Kerexeta.},
title={VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classification of Clinical and Genomic TCGA Data},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF},
year={2020},
pages={413-420},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008951804130420},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF
TI - VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classification of Clinical and Genomic TCGA Data
SN - 978-989-758-398-8
IS - 2184-4305
AU - Garin-Muga, A.
AU - Sucre, A.
AU - Torres, J.
AU - Kerexeta, J.
PY - 2020
SP - 413
EP - 420
DO - 10.5220/0008951804130420
PB - SciTePress