02695nas a2200325   4500008004100000022001400041245009200055210006900147260001200216300001400228490000700242520165100249653002201900653002601922653003301948653003001981653001102011653002102022653001202043653001902055100002002074700002902094700002102123700002102144700001802165700002002183700002502203700001902228856012202247       2020                        eng d  a2168-220800aTowards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.0 aTowards Improving Skin Cancer Diagnosis by Integrating Microarra  c2020 07  a2119-21300 v243 a<p>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.</p>10aBiomarkers, Tumor10aComputational Biology10aDiagnosis, Computer-Assisted10aGene Expression Profiling10aHumans10aMachine Learning10aRNA-seq10aSkin Neoplasms1 aGalvez, Juan, M1 aCastillo-Secilla, Daniel1 aHerrera, Luis, J1 aValenzuela, Olga1 aCaba, Octavio1 aPrados, Jose, C1 aOrtuno, Francisco, M1 aRojas, Ignacio  uhttp://clinbioinfosspa.es/content/towards-improving-skin-cancer-diagnosis-integrating-microarray-and-rna-seq-datasets