Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series

PloS One
Juan Manuel GálvezIgnacio Rojas

Abstract

Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried o...Continue Reading

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Citations

May 15, 2020·Journal of Biophotonics·Albert Van EeckhoutJuan Campos
Dec 20, 2018·Epigenomics·Katerina GrafanakiConstantinos Stathopoulos
Apr 20, 2021·Computers in Biology and Medicine·Daniel Castillo-SecillaIgnacio Rojas

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Datasets Mentioned

BETA
GSE29359
GSE55664

Methods Mentioned

BETA
RNA-seq
chip
chips

Software Mentioned

lumi
virtualArray
Bioconductor
inSilicoDb
Affymetrix
R
lumi package
Bioconductor affy
Bioconductor AnnotationData Packages
Gene Set to Disease ” ( GS2D )

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