A Genetic Algorithm Approach to DNA Microarrays Analysis of Pancreatic Cancer

Melita, Nicolae Teodor; Popescu, Irinel; Holban, Ştefan

doi:10.4316/aece.2008.02008

Cited by 5 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Haploid GAs were previously employed to address feature selection in microarray studies (Melita et al, 2008). In this type of data, the number of samples is significantly lower than the number of features and the utilization of cross validation techniques is necessary for reliable results.…”

Section: Algorithmmentioning

confidence: 99%

dGAselID: An R Package for Selecting a Variable Number of Features in High Dimensional Data

Melita¹,

Holban²

2017

The R Journal

View full text Add to dashboard Cite

The dGAselID package proposes an original approach to feature selection in high dimensional data. The method is built upon a diploid genetic algorithm. The genotype to phenotype mapping is modeled after the Incomplete Dominance Inheritance, overpassing the necessity to define a dominance scheme. The fitness evaluation is done by user selectable supervised classifiers, from a broad range of options. Cross validation options are also accessible. A new approach to crossover, inspired from the random assortment of chromosomes during meiosis is included. Several mutation operators, inspired from genetics, are also proposed. The package is fully compatible with the data formats used in Bioconductor and MLInterfaces package, readily applicable to microarray studies, but is flexible to other feature selection applications from high dimensional data. Several options for the visualization of evolution and outcomes are implemented to facilitate the interpretation of results. The package's functionality is illustrated by examples. Different GA implementations are available as contributors' packages in R. Implementations of GAs for both floating-point and binary chromosomes are included in the genalg (Willighagen and Ballings, 2015) package. The GA (Scrucca, 2016), nsga2R (Tsou, 2015), and gaoptim (Tenorio, 2013) packages are dedicated to optimizations using GAs. A GA designed for determining training populations (Akdemir et al., 2015) is offered in the STPGA package. The package kofnGA (Wolters, 2015) aims to select a fixed-size set of integers. Variable selection applications of GAs are proposed in the

show abstract

Section: Algorithmmentioning

confidence: 99%

dGAselID: An R Package for Selecting a Variable Number of Features in High Dimensional Data

Melita¹,

Holban²

2017

The R Journal

View full text Add to dashboard Cite

show abstract

“…Additionally, after the feature selection process, the system can be better optimized owing to the use of less equipment and computational work such as electrode reduction [4]. It was also used to reduce instrumental costs and processes involving huge data sets such as gene classification [5,6]. The ambition for feature selection is to identify the best descriptive feature or feature subset to improve computational and storage costs, while retaining almost the same or higher classification accuracy [1][2][3][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A fast feature selection approach based on extreme learning machine and coefficient of variation

Ertuğrul¹,

Tağluk²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:Feature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.

show abstract

Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords

Luque‐Baena¹,

Urda²,

Claros³

et al. 2014

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Genetic algorithms are widely used in the estimation of expression profiles from microarrays data. However, these techniques are unable to produce stable and robust solutions suitable to use in clinical and biomedical studies. This paper presents a novel two-stage evolutionary strategy for gene feature selection combining the genetic algorithm with biological information extracted from the KEGG database. A comparative study is carried out over public data from three different types of cancer (leukemia, lung cancer and prostate cancer). Even though the analyses only use features having KEGG information, the results demonstrate that this two-stage evolutionary strategy increased the consistency, robustness and accuracy of a blind discrimination among relapsed and healthy individuals. Therefore, this approach could facilitate the definition of gene signatures for the clinical prognosis and diagnostic of cancer diseases in a near future. Additionally, it could also be used for biological knowledge discovery about the studied disease.

show abstract

A Genetic Algorithm Approach to DNA Microarrays Analysis of Pancreatic Cancer

Cited by 5 publications

References 2 publications

dGAselID: An R Package for Selecting a Variable Number of Features in High Dimensional Data

dGAselID: An R Package for Selecting a Variable Number of Features in High Dimensional Data

A fast feature selection approach based on extreme learning machine and coefficient of variation

Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords

Contact Info

Product

Resources

About