A Comparative Analysis of Biomarker Selection Techniques
| Autor: | Barbara Pes, Nicoletta Dessì, Emanuele Pascariello |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
General Immunology and Microbiology
Article Subject Gene Expression Profiling lcsh:R Stability (learning theory) Computational Biology lcsh:Medicine Context (language use) Genomics Feature selection General Medicine Computational biology Biology Bioinformatics General Biochemistry Genetics and Molecular Biology Gene expression profiling Biomarkers Tumor Humans DNA microarray Biomarker discovery Selection (genetic algorithm) Algorithms Research Article Oligonucleotide Array Sequence Analysis |
| Zdroj: | BioMed Research International, Vol 2013 (2013) BioMed Research International |
| ISSN: | 2314-6141 2314-6133 |
| Popis: | Feature selection has become the essential step in biomarker discovery from high-dimensional genomics data. It is recognized that different feature selection techniques may result in different set of biomarkers, that is, different groups of genes highly correlated to a given pathological condition, but few direct comparisons exist which quantify these differences in a systematic way. In this paper, we propose a general methodology for comparing the outcomes of different selection techniques in the context of biomarker discovery. The comparison is carried out along two dimensions: (i) measuring the similarity/dissimilarity of selected gene sets; (ii) evaluating the implications of these differences in terms of both predictive performance and stability of selected gene sets. As a case study, we considered three benchmarks deriving from DNA microarray experiments and conducted a comparative analysis among eight selection methods, representatives of different classes of feature selection techniques. Our results show that the proposed approach can provide useful insight about the pattern of agreement of biomarker discovery techniques. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|