Machine learning approaches to lung cancer prediction from mass spectra. Export

@article{citeulike:821667, abstract = {We addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z values) versus the number of examples (41). Dimensionality reduction was treated as an integral part of the classification process; variable selection was coupled with model construction in a single ten-fold cross-validation loop. We explored different experimental setups involving two peak height representations, two variable selection methods, and six induction algorithms, all on both the original 1676-mass data set and on a prescreened 124-mass data set. Highest predictive accuracies (1-2 off-sample misclassifications) were achieved by a multilayer perceptron and Na\"{i}ve Bayes, with the latter displaying more consistent performance (hence greater reliability) over varying experimental conditions. We attempted to identify the most discriminant peaks (proteins) on the basis of scores assigned by the two variable selection methods and by neural network based sensitivity analysis. These three scoring schemes consistently ranked four peaks as the most relevant discriminators: 11683, 1403, 17350 and 66107.}, address = {Computer Science Department, University of Geneva, Geneva, Switzerland. melanie.hilario@cui.unige.ch}, author = {Hilario, M. and Kalousis, A. and M\"{u}ller, M. and Pellegrini, C.}, citeulike-article-id = {821667}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/pmic.200300523}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/12973731}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=12973731}, doi = {10.1002/pmic.200300523}, issn = {1615-9853}, journal = {Proteomics}, keywords = {cancer, classification, lung}, month = {September}, number = {9}, pages = {1716--1719}, posted-at = {2007-02-13 08:22:34}, priority = {3}, title = {Machine learning approaches to lung cancer prediction from mass spectra.}, url = {http://dx.doi.org/10.1002/pmic.200300523}, volume = {3}, year = {2003} }

TY - JOUR ID - citeulike:821667 L3 - citeulike-article-id:821667 N2 - We addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z values) versus the number of examples (41). Dimensionality reduction was treated as an integral part of the classification process; variable selection was coupled with model construction in a single ten-fold cross-validation loop. We explored different experimental setups involving two peak height representations, two variable selection methods, and six induction algorithms, all on both the original 1676-mass data set and on a prescreened 124-mass data set. Highest predictive accuracies (1-2 off-sample misclassifications) were achieved by a multilayer perceptron and Naïve Bayes, with the latter displaying more consistent performance (hence greater reliability) over varying experimental conditions. We attempted to identify the most discriminant peaks (proteins) on the basis of scores assigned by the two variable selection methods and by neural network based sensitivity analysis. These three scoring schemes consistently ranked four peaks as the most relevant discriminators: 11683, 1403, 17350 and 66107. IS - 9 JF - Proteomics SN - 1615-9853 AD - Computer Science Department, University of Geneva, Geneva, Switzerland. melanie.hilario@cui.unige.ch EP - 1719 TI - Machine learning approaches to lung cancer prediction from mass spectra. VL - 3 SP - 1716 KW - cancer KW - classification KW - lung AU - Hilario, M AU - Kalousis, A AU - Müller, M AU - Pellegrini, C PY - 2003/09// UR - http://dx.doi.org/10.1002/pmic.200300523 ER -