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Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data |
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| Authors: | Xuegong Zhang Xin Lu Qian Shi Xiu-qin Xu Hon-chiu E Leung Lyndsay N Harris James D Iglehart Alexander Miron Jun S Liu Wing H Wong |
| Affiliation: | (1) Bioinformatics Div, TNLIST and Dept of Automation, Tsinghua University, Beijing, 100084, China;(2) Dept of Biostatistics, Harvard School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA;(3) Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA;(4) Medical Proteomics and Bioanalysis Section, Genome Institute of Singapore, Singapore;(5) Dept of Statistics, Harvard University, 1 Oxford St, Cambridge, MA 02138, USA;(6) Department of Statistics, Stanford University, Stanford, CA 94305, USA |
| Abstract: | BackgroundLike microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. |
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