@misc{10481/96330,
year = {2019},
month = {10},
url = {https://hdl.handle.net/10481/96330},
abstract = {Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.},
organization = {Government of Andalusia under Grant P12–TIC–2082 as part of the development of the research project “Advanced Computer Systems in Applications in the field of Biotechnology and Bioinformatics”},
organization = {Government of Spain under Grant RTI2018-101674-B-I00},
organization = {Feder Andalusia Operational Program Framework under Grant B-TIC-414-UGR18},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {Gene expression},
keywords = {Transcriptomic technologies},
keywords = {Machine learning},
title = {Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets},
doi = {10.1109/JBHI.2019.2953978},
author = {Gálvez Gómez, Juan Manuel and Castillo Secilla, Daniel and Herrera Maldonado, Luis Javier and Valenzuela Cansino, Olga and Caba Pérez, Octavio and Prados Salazar, José Carlos and Ortuño, Francisco M. and Rojas Ruiz, Ignacio},
}