Detecting high-order interactions of single nucleotide polymorphisms using genetic programming

Nunkesser, Robin; Bernholt, Thorsten; Schwender, Holger; Ickstadt, Katja; Wegener, Ingo

doi:10.1093/bioinformatics/btm522

Cited by 55 publications

(45 citation statements)

References 23 publications

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“…The Genetic Programming algorithm GPAS [17] works on case-control genetic association studies. From a computational point of view the tackled problem is to understand a procedure that produces output in B := {0, 1} from inputs from a set P := {0, .…”

Section: Methodsmentioning

confidence: 99%

“…Nunkesser et al [17] have presented a GP algorithm called Genetic Programming for Association Studies (GPAS) which is mainly intended for case-control genetic association studies, i.e. studies where genetic factors that may contribute to a medical condition are identified.…”

Section: Introductionmentioning

confidence: 99%

“…Overviews on approaches for this task are given by Heidema et al [10] and Hoh and Ott [11]. In the data situations considered in [17] GPAS outperforms the known approaches. The main purpose of this paper is to extend and complement the results presented in [17].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of a genetic programming algorithm for association studies

Nunkesser

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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In this paper a Genetic Programming algorithm for genetic association studies is reconsidered. It is shown, that the application field of the algorithm is not restricted to genetic association studies, but that the algorithm can also be applied to logic minimization problems. In the context of multi-valued logic minimization on incompletely specified truth tables it outperforms existing algorithms. In addition, the facilities of the algorithm in the original application field are complemented by new results and experiments. This includes answers to the open questions of how to automatically choose the best individual in the last population and whether crossover is necessary for the algorithm.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of a genetic programming algorithm for association studies

Nunkesser

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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“…Nunkesser et al (2007) describes the use of GP to analyze limited SNP data from the GENICA study (GENICA 1999). Moore et al (2007) has described a system designed to work on full scale "SNP-chip" data.…”

Section: Genetic Studiesmentioning

confidence: 99%

“…The study (Nunkesser et al 2007) is particularly interesting as the authors applied genetic programming to 63 SNPs in a breast cancer study and identified a Boolean rule that includes a known interaction between two SNPs. Notably, introducing several additional SNPs to the rule developed, produced a more powerful predictor.…”

Section: Genetic 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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Classification with High‐Dimensional Genetic Data: Assigning Patients and Genetic Features to Known Classes

2008

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A major task in the statistical analysis of genetic data such as gene expressions and single nucleotide polymorphisms (SNPs) is to predict whether a patient has a certain disease, or from which of several known subtypes of a disease a patient suffers. A large number of discrimination methods have been proposed in the literature and have been applied to genetic data to tackle this task. In this paper, we give an overview on the most popular of these procedures in the analysis of genetic data. Moreover, we describe how these methods for supervised classification can be combined with variable selection approaches to reduce the number of genetic features from several thousands to as few as possible to form a concise classification rule. Finally, we show how the resulting statistical models can be validated.

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Detecting high-order interactions of single nucleotide polymorphisms using genetic programming

Abstract: Software can be downloaded from http://ls2-www.cs.uni-dortmund.de/~nunkesser/#Software

Cited by 55 publications

References 23 publications

Analysis of a genetic programming algorithm for association studies

Analysis of a genetic programming algorithm for association studies

Applications of genetic programming in cancer research

Classification with High‐Dimensional Genetic Data: Assigning Patients and Genetic Features to Known Classes

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