Data Mining Methods and Models

Larose, Daniel T.

doi:10.1002/0471756482

Cited by 243 publications

(221 citation statements)

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“…The regression coefficients of classificators were estimated with the application of the maximum likelihood method [22]. That method does not provide an analytical solution.…”

Section: Methods and Resultsmentioning

confidence: 99%

Empirical Formulas for the Calculations of the Hardness of Steels Cooled From the Austenitizing Temperature

Trzaska¹

2016

Archives of Metallurgy and Materials

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In this paper, the equations applied for the purpose of the calculations of the hardness of continuously cooled structural steels upon the basis of the temperature of austenitizing. The independent variables of the hardness model were: the mass concentrations of elements, the austenitizing temperature and the cooling rate. The equations were developed with the application of the following methods: multiple regression and logistic regression. In this paper, attention was paid to preparing data for the purpose of calculations, to the methodology of the calculations, and also to the assessment of the quality of developed formulas. The collection of empirical data was prepared upon the basis of more than 500 CCT diagrams.

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“…The regression coefficients of classificators were estimated with the application of the maximum likelihood method [22]. That method does not provide an analytical solution.…”

Section: Methods and Resultsmentioning

confidence: 99%

Empirical Formulas for the Calculations of the Hardness of Steels Cooled From the Austenitizing Temperature

Trzaska¹

2016

Archives of Metallurgy and Materials

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“…Factor analysis (FA), another important analytical tool, identifies common factors that capture variance-covariance of multiple variables with random error. PCA, in contrast, identifies principle components, with the restriction that random error must be zero[31]. Therefore, FA could be better suited to the analysis of observed traits with measured errors and to tests of genetic pleiotropy in some cases.…”

Section: Discussionmentioning

confidence: 99%

Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components

et al. 2010

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BackgroundQuantitative traits often underlie risk for complex diseases. For example, weight and body mass index (BMI) underlie the human abdominal obesity-metabolic syndrome. Many attempts have been made to identify quantitative trait loci (QTL) over the past decade, including association studies. However, a single QTL is often capable of affecting multiple traits, a quality known as gene pleiotropy. Gene pleiotropy may therefore cause a loss of power in association studies focused only on a single trait, whether based on single or multiple markers.ResultsWe propose using principal-component-based multivariate regression (PCBMR) to test for gene pleiotropy with comprehensive evaluation. This method generates one or more independent canonical variables based on the principal components of original traits and conducts a multivariate regression to test for association with these new variables. Systematic simulation studies have shown that PCBMR has great power. PCBMR-based pleiotropic association studies of abdominal obesity-metabolic syndrome and its possible linkage to chromosomal band 3q27 identified 11 susceptibility genes with significant associations. Whereas some of these genes had been previously reported to be associated with metabolic traits, others had never been identified as metabolism-associated genes.ConclusionsPCBMR is a computationally efficient and powerful test for gene pleiotropy. Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.

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“…We emphasize that MAHATMA has two kinds of operators: structural operators (usual in GP Koza 1992)) and leaf operators (based on genetic algorithms (Goldberg 1989;Larose 2006)). The structural operators are: reproduction, crossover, mutation, editing and encapsulation.…”

Section: Selection Methods and Genetic Operatorsmentioning

confidence: 99%

A genetic programming method for protein motif discovery and protein classification

2010

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Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents MAHATMA -Memetic Algorithm-based Highly Adapted Tool for Motif Ascertainment -a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on Genetic Programming using operators specially tailored for the target problem. The final performance was measured using sensitivity (Se), specificity (Sp) and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is effective in finding predictive features (motifs) for protein classification.

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Data Mining Methods and Models

Cited by 243 publications

References 0 publications

Empirical Formulas for the Calculations of the Hardness of Steels Cooled From the Austenitizing Temperature

Empirical Formulas for the Calculations of the Hardness of Steels Cooled From the Austenitizing Temperature

Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components

A genetic programming method for protein motif discovery and protein classification

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