6533b857fe1ef96bd12b4712

RESEARCH PRODUCT

Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method

Jesús Vicente De Julián-ortizEmili Besalú

subject

Male0301 basic medicineKey genesComputer sciencelcsh:QR1-502Binary numberBiochemistrylcsh:MicrobiologyArticlePattern Recognition AutomatedStructure-Activity Relationship03 medical and health sciencesBig data0302 clinical medicinerankingData MiningHumanscancergene expressionsRelated geneCàncerMolecular BiologyOligonucleotide Array Sequence AnalysisCancerPròstata -- CàncerLeukemiaReceiver operating characteristicbusiness.industryGene Expression ProfilingleukemiaProstatic NeoplasmsLeucèmiaDades massivesPattern recognitionprostate cancerExpressió gènicaSSIR method030104 developmental biologyROC Curvemultilevel fingerprintsExpression dataData Interpretation Statistical030220 oncology & carcinogenesisProstate -- CancerArtificial intelligenceGene expressionbusinessAlgorithms

description

The Superposing Significant Interaction Rules (SSIR) method is a combinatorial procedure that deals with symbolic descriptors of samples. It is able to rank the series of samples when those items are classified into two classes. The method selects preferential descriptors and, with them, generates rules that make up the rank by means of a simple voting procedure. Here, two application examples are provided. In both cases, binary or multilevel strings encoding gene expressions are considered as descriptors. It is shown how the SSIR procedure is useful for ranking the series of patient transcription data to diagnose two types of cancer (leukemia and prostate cancer) obtaining Area Under Receiver Operating Characteristic (AU-ROC) values of 0.95 (leukemia prediction) and 0.80&ndash

yearjournalcountryeditionlanguage
2020-09-08
10.3390/biom10091293http://hdl.handle.net/10256/18434