Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines

Peng, Syd S.; Xu, Qianghua; Ling, Xuefeng B.; Peng, Xiaoning; Du, Wei; Chen, Liangbiao

doi:10.1016/s0014-5793(03)01275-4

Cited by 182 publications

(113 citation statements)

References 17 publications

(39 reference statements)

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“…The maximum classification rate we obtain is 1 using 3 genes while the corresponding average classification rate is 1 and the corresponding average dimension is 3.6 (see Table 3 for details). The same performance is achieved by [1] [2] [8], even if the number of genes selected by [1] [2] [8] is greater than the one obtained by our method.…”

Section: Discussion and Concluding Remarkssupporting

confidence: 57%

“…Recent studies [1][2] [3][4] address the problem of gene selection using a standard GA which evolves populations of possible solutions, the quality of each solution being evaluated by an SVM classifier. GAs have been employed in conjunction with different classifiers, such as k-Nearest Neighbor in [6] and Neural Networks in [7].…”

Section: Related Workmentioning

confidence: 99%

“…In particular, recent literature [1] [2][3] [4] demonstrates their success on micro-array data analysis. The micro-arrays provide a view onto cellular organization of life through quantitative data on gene expression levels and it is expected that knowledge gleaned from micro-array data will contribute significantly to advances in fundamental questions in biology as well as in clinical medicine.…”

Section: Introductionmentioning

confidence: 99%

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A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data

Cannas

Dessì

Pes

2010

Intelligent Information Processing V

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Abstract. Evolutionary algorithms have received much attention in extracting knowledge on high-dimensional micro-array data, being crucial to their success a suitable definition of the search space of the potential solutions. In this paper, we present an evolutionary approach for selecting informative genes (features) to predict and diagnose cancer. We propose a procedure that combines results of filter methods, which are commonly used in the field of data mining, to reduce the search space where a genetic algorithm looks for solutions (i.e. gene subsets) with better classification performance, being the quality (fitness) of each solution evaluated by a classification method. The methodology is quite general because any classification algorithm could be incorporated as well a variety of filter methods. Extensive experiments on a public micro-array dataset are presented using four popular filter methods and SVM.

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Section: Discussion and Concluding Remarkssupporting

confidence: 57%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data

Cannas

Dessì

Pes

2010

Intelligent Information Processing V

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“…After learning the features of the class, the SVM recognizes unknown samples as a member of a specific class. SVMs have been shown to perform especially well in multiple areas of biological analyses, especially functional class prediction from microarray gene expression data and chemometrics (24)(25)(26)(27)(28). We constructed an SVM classifier with a nonlinear algorithm with Matlab (version 6.5) (Mathworks, Natick, MA) using the training set of sensor response data from subjects with lung cancer, subjects with noncancer disease, and healthy control subjects.…”

Section: Svm Analysismentioning

confidence: 99%

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Machado

Laskowski²,

Deffenderfer³

et al. 2005

Am J Respir Crit Care Med

527

429

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Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer.

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“…In this study, we investigate the feasibility of genetic algorithms (GA), one kind of variable selection algorithms, in classifying three varieties of paper based on 150 IR spectra.A GA procedure has been used to solve variable selection problems effectively [13][14][15]. It is based on the principles of evolutionary biology in which members of a space of candidate solutions, i.e.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis

Liong¹,

Lee²,

Osman³

et al. 2016

AIP Conference Proceedings

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Abstract. Selection of the most significant variables, i.e. the wavenumber, from an infrared (IR) spectrum is always difficult to be achieved. In this preliminary paper, the feasibility of genetic algorithms (GA) in identifying most informative wavenumbers from 150 IR spectra of papers was investigated. The list of selected wavenumbers was then employed in Linear Discriminant Analysis (LDA). GA procedure was repeated 30 times to get different lists of variables. Then the performances of LDA models were estimated via leave-one-out cross-validation. A total of six to eight wavenumbers were identified to be valuable variables in the GA procedures. All the 30 LDA models achieve correct classification rates between 97.3% to 100.0%. Therefore the GA-LDA model could be a suitable tool for differentiating white papers that appeared to be highly similar in their IR fingerprints.

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Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines

Cited by 182 publications

References 17 publications

A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data

A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis

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