Abstract
A number of studies have been performed with the objective of applying various artificial intelligence techniques to the prediction and classification of cancer specific biomarkers for use in clinical diagnosis. Most biological data, such as that obtained from SELDI-TOF (Surface Enhanced Laser Desorption and Ionization-Time Of Flight) MS (Mass Spectrometry) is high dimensional, and therefore requires dimension reduction in order to limit the computational complexity and cost. The DT (Decision Tree) is an algorithm which allows for the fast classification and effective dimension reduction of high dimensional data. However, it does not guarantee the reliability of the features selected by the process of dimension reduction. Another approach is the MLP (Multi-Layer Perceptron) which is often more accurate at classifying data, but is not suitable for the processing of high dimensional data. In this paper, we propose on a novel approach, which is able to accurately classify prostate cancer SELDI data into normal and abnormal classes and to identify the potential biomarkers. In this approach, we first select those features that have excellent discrimination power by using the DT. These selected features constitute the potential biomarkers. Next, we classify the selected features into normal and abnormal categories by using the MLP; at this stage we repeatedly perform cross validation to evaluate the propriety of the selected features. In this way, the proposed algorithm can take advantage of both the DT and MLP, by hybridizing these two algorithms. The experimental results demonstrate that the proposed algorithm is able to identify multiple potential biomarkers that enhance the confidence of diagnosis, also showing better specificity, sensitivity and learning error rates than other algorithms. The proposed algorithm represents a promising approach to the identification of proteomic patterns in serum that can distinguish cancer from normal or benign and is applicable to clinical diagnosis and prognosis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adam, B., Qu, Y., Davis, J., et al.: Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Research 62, 3609–3614 (2002)
Petricoin III, E., Ardekani, A., Hitt, B., et al.: Use of proteomic patterns in serum to identify ovarian cancer. The Lancet 359, 572–577 (2002)
Ball, G., Mian, S., Holding, F., et al.: An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumors and rapid identification of potential biomarkers. Bioinformatics 18(3), 395–404 (2002)
Duda, R., Hart, P., Stork, D.: Pattern classification. Wiley-Interscience, New York (2001)
Merchant, M., Weinberger, S.R.: Recent advancements in surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Electrophoresis 21, 1164–1177 (2000)
Rogers, M.A., Clarke, P., Noble, J., Munro, N.P., Paul, A., Selby, P.J., Banks, R.E.: Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: identification of key issues affecting potential clinical utility. In: Cancer research, October 2003, pp. 6971–6983 (2003)
Qu, Y., Adam, B.-L., Yasui, Y., Ward, M.D., Cazares, L.H., Schellhammer, P.F., Feng, Z., Semmes, O.J., Wright, G.L.: Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin. Chem. 48, 1835–1843 (2002)
Won, Y., Song, H.-J., Kang, T.-W., Kim, J.J., Han, B.D., Lee, S.W.: Pattern Analysis of Serum Proteome Distinguishes Renal Cell Carcinoma from Other Renal Diseases and Healthy Persons. In: Proteomics, December 2003, pp. 2310–2316 (2003)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann Publisher, San Francisco (1993)
Wagner, M., Naik, D.N., Pothen, A., Kasukurti, S., Devineni, R.R., Adam, B.-L., Semmes, O.J., Wright Jr., G.L.: Computational protein biomarker prediction: a case study for prostate cancer. BMC Bioinformatics (May 2004)
Kim, J.-J., Won, Y., Kim, Y.-H.: Proteomic Pattern Classification using Bio-markers for Prostate Cancer Diagnosis. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 631–638. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, JJ., Kim, YH., Won, Y. (2005). A Hybrid Classification System for Cancer Diagnosis with Proteomic Bio-markers. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_17
Download citation
DOI: https://doi.org/10.1007/11595014_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
Online ISBN: 978-3-540-31646-6
eBook Packages: Computer ScienceComputer Science (R0)