Lymphoma Cancer Classification Using Genetic Programming with SNR Features

Hong, Jin-Hyuk; Cho, Sung Bae

doi:10.1007/978-3-540-24650-3_8

Cited by 28 publications

(20 citation statements)

References 14 publications

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“…Abundant applications can be found in bioinformatics on quantitative structure-activity analysis in drug design, cancer classification from gene expression data, classification of genetically-modified organisms, and classification of cognitive states from fMRI data [49], [33], [52], [5], [37], [32], [13], [28], [8]. This paper provides a new example of how GP techniques can be employed to generate predictive features from sequence data.…”

Section: Related Ea Workmentioning

confidence: 99%

An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction

Kamath

Compton²,

Doğan

et al. 2012

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-Associating functional information with biological sequences remains a challenge for machine learning methods. The performance of these methods often depends on deriving predictive features from the sequences sought to be classified. Feature generation is a difficult problem, as the connection between the sequence features and the sought property is not known a priori. It is often the task of domain experts or exhaustive feature enumeration techniques to generate a few features whose predictive power is then tested in the context of classification. This paper proposes an evolutionary algorithm to effectively explore a large feature space and generate predictive features from sequence data. The effectiveness of the algorithm is demonstrated on an important component of the gene-finding problem, DNA splice site prediction. This application is chosen due to the complexity of the features needed to obtain high classification accuracy and precision. Our results test the effectiveness of the obtained features in the context of classification by Support Vector Machines and show significant improvement in accuracy and precision over state-of-the-art approaches.

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Section: Related Ea Workmentioning

confidence: 99%

An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction

Kamath

Compton²,

Doğan

et al. 2012

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…A number of studies have been published on the use of genetic programming to classify tumors on the basis of genomic signatures (Gilbert et al 2000, Moore and Parker 2002, Driscoll et al 2003, Hong and Cho 2004, Mitra et al 2006, Yu et al 2007. Because of GP's unbiased feature selection and ability to mathematically combine expression levels, the classification models produced are quite parsimonious and able to capture maximum information from a small number of features.…”

Section: Genomic Studiesmentioning

confidence: 99%

Applications of genetic programming in cancer research

Worzel

Almal

et al. 2009

The International Journal of Biochemistry & Cell Biology

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The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allows scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

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“…It captures the expression levels of thousands of genes simultaneously which contain information on diseases [15]. Since finding an understandable classification rule is required beside the accuracy, discovering classification rules using genetic programming was studied in the previous work [16]. Even though the rule is quite simple, it shows a good performance in classifying cancers.…”

Section: Cancer Classification Using Genetic Programmingmentioning

confidence: 99%

Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

Hong

Cho

2005

Modeling Decisions for Artificial Intelligence

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Abstract. Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines a set of diverse rules obtained by genetic programming. Genetic programming generates interpretable classification rules, and diversity among them is directly estimated. Finally, several diverse rules are combined by a fusion method to generate a final decision. The proposed method has been applied to cancer classification using gene expression profiles, which is one of the important issues in bioinformatics. Experiments on several popular cancer datasets have demonstrated the usability of the method. High performance of the proposed method has been obtained, and the accuracy has increased by diversity among the base classification rules.

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Lymphoma Cancer Classification Using Genetic Programming with SNR Features

Cited by 28 publications

References 14 publications

An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction

An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction

Applications of genetic programming in cancer research

Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

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