Genetic Programming for Biomarker Detection in Mass Spectrometry Data

Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

doi:10.1007/978-3-642-35101-3_23

Cited by 13 publications

(13 citation statements)

References 22 publications

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“…Furthermore, when and how to apply these operators, and the parameter settings in GAs are also key factors influencing their performance on feature selection. [19], [20], [40], [113], [114], [115], [116], [117], [15], [118], [119], [120], [121], [122], [123], [124], [125], [126] Filter [41], [127], [128] [129], [130] B. GP for Feature Selection Table II shows typical works on GP for feature selection. Compared with GAs and PSO, there are a much smaller number of works on GP for feature selection.…”

Section: A Gas For Feature Selectionmentioning

confidence: 99%

“…When the number of instances is too small, it is hard to design appropriate experiments to test the performance of the algorithms. For example, there might be tens of thousands of features, but the number of instances can be smaller than one hundred because of the high cost of collecting such instances [117]. It is difficult to split the data into a training set and a test set to represent the actual problem.…”

Section: H Number Of Instancesmentioning

confidence: 99%

“…It is difficult to split the data into a training set and a test set to represent the actual problem. Therefore, many existing works have the problem of feature selection bias [248], [249], especially when the whole set of data is used during the feature selection process [44], [70], [117], [145], [189], [229], [215]. Although cross-validation or bootstrap sampling techniques [250] can address the issue to some extent, they may have the problem of it being hard to decide the final selection of individual features because EC algorithms (and conventional deterministic algorithms) often select different features from different cross-validation runs.…”

Section: H Number Of Instancesmentioning

confidence: 99%

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A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

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1,472

635

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Section: A Gas For Feature Selectionmentioning

confidence: 99%

Section: H Number Of Instancesmentioning

confidence: 99%

Section: H Number Of Instancesmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

Self Cite

1,472

635

View full text Add to dashboard Cite

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“…In this study, the detection and quantification were done by performing a regression process and the values estimated by GP were compared with the expected values of sucrose. In our previous research, we used the features selected by GP with different fitness functions and achieved a certain level of success on MS and LC-MS data sets (Ahmed et al, 2012(Ahmed et al, , 2013a. Moreover, in Ahmed et al (2013b), different feature selection metrics were used with GP to further select a smaller number of features.…”

Section: Related Workmentioning

confidence: 99%

“…MS has proven to be extremely useful for the biomarker detection task and identification of the proteins or metabolites (Ahmed et al, 2012). However, running MS is a time-consuming process and it is not practical to produce a large number of samples.…”

Section: Mass Spectrometrymentioning

confidence: 99%

Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

2014

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Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain-(IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.

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