A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{conf/evoW/AhmedZPX16,

• author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue",
• title = "A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data",
• booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016",
• year = "2016",
• editor = "Giovanni Squillero and Paolo Burelli",
• volume = "9597",
• series = "Lecture Notes in Computer Science",
• pages = "106--122",
• address = "Porto, Portugal",
• month = mar # " 30 -- " # apr # " 1",
• organisation = "EvoStar",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2016-03-23",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#AhmedZPX16",
• isbn13 = "978-3-319-31204-0",
• DOI = "doi:10.1007/978-3-319-31204-0_8",
• abstract = "Mass spectrometry is currently the most commonly used technology in biochemical research for proteomic analysis. The main goal of proteomic profiling using mass spectrometry is the classification of samples from different clinical states. This requires the identification of proteins or peptides (biomarkers) that are expressed differentially between different clinical states. However, due to the high dimensionality of the data and the small number of samples, classification of mass spectrometry data is a challenging task. Therefore, an effective feature manipulation algorithm either through feature selection or construction is needed to enhance the classification performance and at the same time minimise the number of features. Most of the feature manipulation methods for mass spectrometry data treat this problem as a single objective task which focuses on improving the classification performance. This paper presents two new methods for biomarker detection through multi-objective feature selection and feature construction. The results show that the proposed multi-objective feature selection method can obtain better subsets of features than the single-objective algorithm and two traditional multi-objective approaches for feature selection. Moreover, the multi-objective feature construction algorithm further improves the performance over the multi-objective feature selection algorithm. This paper is the first multi-objective genetic programming approach for biomarker detection in mass spectrometry data",
• notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",

}

Genetic Programming entries for Soha Ahmed Mengjie Zhang Lifeng Peng Bing Xue

Citations