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Comparing the dimensionality reduction methods in gene expression databases
Authors:Helyane Bronoski Borges  Júlio Cesar Nievola
Affiliation:1. Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA;2. The Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA;3. Department of Obstetrics and Gynecology, Division of Maternal–Fetal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA;4. Department of Obstetrics and Gynecology, Faculty of Medicine, São João Hospital and Institute of Biomedical Engineering, University of Porto, Porto, Portugal;1. Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX 77555, United States;2. Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX 77555, United States;3. Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, TX 77555, United States;4. Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX 77555, United States;5. Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX 77555, United States;6. Optical Microscopy Core, University of Texas Medical Branch, Galveston, TX 77555, United States
Abstract:Dimensionality reduction has been applied in the most different areas, among which the data analysis of gene expression obtained with the microarray approach. The data involved in this problem is challenging for machine learning algorithms due to a small number of samples and a high number of attributes. This paper proposes a preprocessing phase by means of attribute selection and random projection method in microarray data. Experimental results are promising and show that the use of these methods improves the performance of classification algorithms.
Keywords:
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