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

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 r...

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Bibliographic Details
Published inBiomolecules (Basel, Switzerland) Vol. 10; no. 9; p. 1293
Main Authors Besalú, Emili, De Julián-Ortiz, Jesus Vicente
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 08.09.2020
MDPI
Subjects
Algorithms
Androgens
Biomarkers
cancer
Classification
Data Interpretation, Statistical
Data Mining - methods
Datasets
Deoxyribonucleic acid
Design of experiments
DNA
Feature selection
Gene expression
Gene Expression Profiling
gene expressions
Genomics
Health aspects
Humans
Leukemia
Leukemia - classification
Leukemia - genetics
Male
Methods
Mortality
multilevel fingerprints
Mutation
Oligonucleotide Array Sequence Analysis - methods
Pattern Recognition, Automated - methods
Prostate cancer
Prostatic Neoplasms - classification
Prostatic Neoplasms - genetics
ROC Curve
SSIR method
Structure-Activity Relationship
Transcription
Taiwan
leukemia
prostate cancer
cancer
gene expressions
ranking
SSIR method
multilevel fingerprints
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Summary: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 0.80-0.90 (prostate). The preferential selected descriptors here are specific gene expressions, and this is potentially useful to point to possible key genes.
Bibliography:ObjectType-Article-1
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ISSN:2218-273X
2218-273X
DOI:10.3390/biom10091293