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A Comparison of Methods for Classifying Clinical Samples Based on Proteomics Data: A Case Study for Statistical and Machine Learning Approaches

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  • Dayle L Sampson
  • Tony J Parker
  • Zee Upton
  • Cameron P Hurst

Abstract

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Suggested Citation

  • Dayle L Sampson & Tony J Parker & Zee Upton & Cameron P Hurst, 2011. "A Comparison of Methods for Classifying Clinical Samples Based on Proteomics Data: A Case Study for Statistical and Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
  • Handle: RePEc:plo:pone00:0024973
    DOI: 10.1371/journal.pone.0024973
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    References listed on IDEAS

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    1. Ruedi Aebersold & Matthias Mann, 2003. "Mass spectrometry-based proteomics," Nature, Nature, vol. 422(6928), pages 198-207, March.
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    1. Hon-Yi Shi & Hao-Hsien Lee & Jinn-Tsong Tsai & Wen-Hsien Ho & Chieh-Fan Chen & King-Teh Lee & Chong-Chi Chiu, 2012. "Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    2. Jin-Xing Liu & Yong Xu & Chun-Hou Zheng & Yi Wang & Jing-Yu Yang, 2012. "Characteristic Gene Selection via Weighting Principal Components by Singular Values," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    3. Shiva Kalantari & Dorothea Rutishauser & Shiva Samavat & Mohsen Nafar & Leyla Mahmudieh & Mostafa Rezaei-Tavirani & Roman A Zubarev, 2013. "Urinary Prognostic Biomarkers and Classification of IgA Nephropathy by High Resolution Mass Spectrometry Coupled with Liquid Chromatography," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.

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