Data-Based Fuzzy Modeling for Complex Applications

Kiendl, H.; Krause, Peter; Schauten, Daniel; Slawinski, T.

doi:10.1007/978-3-662-05609-7_3

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…These rules were ranked using the relevance index RI r introduced by Kiendl and others [39-43]. Here, a rule ‘IF P r THEN C r ’ is ranked on the basis of RI r .…”

Section: Methodsmentioning

confidence: 99%

“…To select interesting rules from the set of all possible rules, constraints on various measures of significance can be used, such as thresholds on support and confidence. In our hands [ 38 ], the relevance index introduced by Kiendl and coworkers [ 39 - 43 ] is able to generate robust rule sets with high predictive strength from data of high dimension (for example, number of genes) but of low sample number. A deterministic decision rule R r is defined by ‘IF P r ( y ) THEN C r ’, where P r describes a premise evaluating the observations y (that is, the enhanced expression of a given gene) and C r is the set of possible conclusions (for example, the prediction of a disease status of a given individual).…”

Section: Introductionmentioning

confidence: 99%

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Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

et al. 2014

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IntroductionDiscrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory or degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers.MethodsThree multi-center genome-wide transcriptomic data sets (Affymetrix HG-U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule-based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.ResultsThe optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways.ConclusionFirst-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.

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“…These rules were ranked using the relevance index RI r introduced by Kiendl and others [39-43]. Here, a rule ‘IF P r THEN C r ’ is ranked on the basis of RI r .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

et al. 2014

View full text Add to dashboard Cite

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“…In recent years, several machine learning (ML) algorithms have been successfully applied for automated generation of predictive models from data (Kiendl et al, 2003;Kononenko, 2001).…”

Section: Introductionmentioning

confidence: 99%

Integrating Knowledge-Driven and Data-Driven Approaches for the Derivation of Clinical Prediction Rules

Kwiatkowska

Atkins

Ayas³

et al.

Fourth International Conference on Machine Learning and Applications (ICMLA'05)

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The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection (currently available to the public at the "Institutional Repository" link of the SFU Library website at: ) and, without changing the content, to translate the thesislproject or extended essays, if technically possible, to any medium or format for the purpose of preservation of the digital work.The author has further agreed that permission for multiple copying of this work for scholarly purposes may be granted by either the author or the Dean of Graduate Studies.It is understood that copying or publication of this work for financial gain shall not be allowed without the author's written permission.

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Data-Based Technique for Extracting Knowledge from Data Generated in Experiments

Zaporojan

Carbune

Calmicov

2020

2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP)

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Data-Based Fuzzy Modeling for Complex Applications

Cited by 3 publications

References 11 publications

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

Integrating Knowledge-Driven and Data-Driven Approaches for the Derivation of Clinical Prediction Rules

Data-Based Technique for Extracting Knowledge from Data Generated in Experiments

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