Mass spectrometry cancer data classification using wavelets and genetic algorithm Nguyen, Thanh, Nahavandi, Saeid, Creighton, Douglas and Khosravi, Abbas 2015, Mass spectrometry cancer data classification using wavelets and genetic algorithm, FEBS letters, vol. 589, no. 24 part b, pp. 3879-3886, doi: 10.1016/j.febslet.2015.11.019. Attached Files Name Description MIMEType Size Downloads Title Mass spectrometry cancer data classification using wavelets and genetic algorithm Author(s) Nguyen, Thanh orcid.org/0000-0001-9709-1663 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Creighton, Douglas orcid.org/0000-0002-9217-1231 Khosravi, Abbas orcid.org/0000-0001-6927-0744 Journal name FEBS letters Volume number 589 Issue number 24 part b Start page 3879 End page 3886 Total pages 8 Publisher Elseiver Place of publication Amsterdam, The Netherlands Publication date 2015-12-21 ISSN 1873-3468 Keyword(s) Cancer classification Feature extraction Genetic algorithm Mass spectrometry data Wavelet transformation Summary This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners. Language eng DOI 10.1016/j.febslet.2015.11.019 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation 080109 Pattern Recognition and Data Mining 0601 Biochemistry And Cell Biology 0304 Medicinal And Biomolecular Chemistry Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2015, Elsevier Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30080140 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection GTP Research Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Author URL Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Tue, 08 Dec 2015, 12:18:54 EST Wed, 09 Dec 2015, 04:02:29 EST Wed, 09 Dec 2015, 05:13:33 EST Thu, 10 Dec 2015, 13:22:56 EST Thu, 17 Dec 2015, 07:29:02 EST Mon, 18 Jul 2016, 15:51:09 EST Tue, 26 Jul 2016, 08:25:54 EST Tue, 26 Jul 2016, 08:26:42 EST Wed, 12 Oct 2016, 14:25:41 EST Wed, 09 Nov 2016, 11:38:27 EST Wed, 01 Mar 2017, 07:32:14 EST Sat, 10 Oct 2020, 09:36:25 EST Filtered Full Citation counts:   Cited 14 times in TR Web of Science Cited 19 times in Scopus Search Google Scholar Access Statistics: 544 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Tue, 08 Dec 2015, 12:18:54 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.