Search results for "SSIR method"

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

2020

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 Recei…

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
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Superposing significant interaction rules (SSIR) method: a simple procedure for rapid ranking of congeneric compounds

2020

The Superposing Significant Interaction Rules (SSIR) method is revised and implemented. The method is a simple combinatorial procedure, which deals with in situ generated rules among a dichotomized congeneric molecular family, selecting the most probabilistically relevant ones. The mere counting of the number of relevant rules attached to new compounds generates a molecular ranking useful for database filtering, refinement and prediction. The algorithm only needs for a symbolic molecular representation and this allows for mining the database in a confidential manner. Third parties will not know the real compounds that are on the way to be worked out. The procedure is tested for a complete s…

Simple (abstract algebra)Computer sciencebusiness.industryQuímica combinatòriaPattern recognitionCombinatorial chemistrySSIR method; Congener series; Ranking; SAR; Balanced Leave-two-out cross validation (BL2O)General ChemistryArtificial intelligenceQuímicabusinessRanking (information retrieval)
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