Journal ArticleDOI
Selecting a minimal number of relevant genes from microarray data to design accurate tissue classifiers.
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TLDR
An efficient evolutionary approach to gene selection from microarray data which can be combined with the optimal design of various multiclass classifiers is proposed which is superior to GA/MLHD in terms of the number ofselected genes, classification accuracy, and robustness of selected genes and accuracy.Abstract:
It is essential to select a minimal number of relevant genes from microarray data while maximizing classification accuracy for the development of inexpensive diagnostic tests. However, it is intractable to simultaneously optimize gene selection and classification accuracy that is a large parameter optimization problem. We propose an efficient evolutionary approach to gene selection from microarray data which can be combined with the optimal design of various multiclass classifiers. The proposed method (named GeneSelect) consists of three parts which are fully cooperated: an efficient encoding scheme of candidate solutions, a generalized fitness function, and an intelligent genetic algorithm (IGA). An existing hybrid approach based on genetic algorithm and maximum likelihood classification (GA/MLHD) is proposed to select a small number of relevant genes for accurate classification of samples. To evaluate the performance of GeneSelect, the gene selection is combined with the same maximum likelihood classification (named IGA/MLHD) for convenient comparisons. The performance of IGA/MLHD is applied to 11 cancer-related human gene expression datasets. The simulation results show that IGA/MLHD is superior to GA/MLHD in terms of the number of selected genes, classification accuracy, and robustness of selected genes and accuracy.read more
Citations
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Journal ArticleDOI
Genetic Bee Colony (GBC) algorithm
TL;DR: The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes, which proves that the GBC algorithms is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.
Journal ArticleDOI
mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.
TL;DR: The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods, showing that mR MR-ABC is a promising approach for solving gene selection and cancer classification problems.
Journal Article
IG-GA: A Hybrid Filter/Wrapper Method for Feature Selection of Microarray Data
TL;DR: Experimental results show that the proposed filter method and wrapper method for feature selection in microarray data sets simplifies the number of gene expression levels effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods.
Journal ArticleDOI
Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction
TL;DR: Experiments showed that the proposed ensemble of probabilistic neural network (PNN) and neighborhood rough set model based gene reduction approach to tumor classification can obtain both high and stable classification performance, which is not too sensitive to the number of initially selected genes and competitive to most existing methods.
Journal ArticleDOI
Gene selection and classification using Taguchi chaotic binary particle swarm optimization
TL;DR: Experimental results show that this hybrid method effectively simplifies features selection by reducing the number of features needed, and could constitute a valuable tool for gene expression analysis in future studies.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
Javed Khan,Jun S. Wei,Markus Ringnér,Markus Ringnér,Lao H. Saal,Marc Ladanyi,Frank Westermann,Frank Berthold,Manfred Schwab,Cristina R. Antonescu,Carsten Peterson,Paul S. Meltzer +11 more
TL;DR: The ability of the trained ANN models to recognize SRBCTs is demonstrated, and the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy are demonstrated.