Abstract
The vast amount of data on gene expression that is now available through high-throughput measurement of mRNA abundance has provided a new basis for disease diagnosis. Microarray-based classification of disease states is based on gene expression profiles of patients. A large number of methods have been proposed to identify diagnostic markers that can accurately discriminate between different classes of a disease. Using only a subset of genes in the pathway, such as so-called condition-responsive genes (CORGs), may not fully represent the two classification boundaries for Case and Control classes. Negatively correlated feature sets (NCFS) for identifying CORGs and inferring pathway activities are proposed in this study. Our two proposed methods (NCFS-i and NCFS-c) achieve higher accuracy in disease classification and can identify more phenotype-correlated genes in each pathway when comparing to several existing pathway activity inference methods.
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.
References
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gassenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)
Young, R.A.: Biomedical Discovery with DNA Arrays: Cell 102, pp. 9–15 (2000)
Lakhai, S., Ashworth, A.: Microarray and Histopathological Analysis of Tumours: The Future the Past? Nat. Rev. Can. 1, 151–157 (2001)
Berns, A.: Cancer: Gene Expression Diagnosis. Nature 403, 491–492 (2000)
Su, A.I., Welsh, J.B., Sapinoso, L.M., Kern, S.G., Dimitrov, P., Lapp, H., Schultz, P.G., Powell, S.M., Moskaluk, C.A., Frierson Jr., H.F., Hampton, G.M.: Molecular Classification of Human Carcinomas by Use of Gene Expression Signatures. Cancer Res. 61, 7388–7393 (2001)
Lu, Y., Han, J.: Cancer Classification Using Gene Expression Data. Inform. Systems 28, 243–268 (2008)
Ein-Dor, L., Suk, O., Domany, E.: Thousands of Samples Are Needed to Generate a Robust Gene List for Predicting Outcome in Cancer. Proc. Natl. Acad. Sci. USA 103, 5923–5928 (2006)
Dupuy, A., Simon, R.M.: Critical Review of Published Miroarray Studies for Cancer Outcome and Guidelines on Statistical Analysis and Reporting. J. Natl. Cancer Inst. 99, 147–157 (2007)
Michiels, S., Koscielny, S., Hill, C.: Prediction of Cancer Outcome with Microarrays: A Multiple Random Validation Strategy. Lancet 365, 488–492 (2005)
Vogelstein, B., Kinzler, K.W.: Cancer Genes and the Pathways They Control. Nat. Med. 10, 789–799 (2004)
Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., Yamanishi, Y.: KEGG for Linking Genomes to Life and the Environment. Nucleic Acids Res. 36, D480–D484 (2008)
Guo, Z., Zhang, T., Li, X., Wang, Q., Xu, J., Yu, H., Zhu, J., Wang, H., Wang, C., Topol, E.J., Wang, Q., Rao, S.: Towards Precise Classification of Cancers Based on Robust Gene Functional Expression Profiles. BMC Bioinformatics 6 (2005)
Bild, A.H., Yao, G., Chang, J.T., Wang, Q., Potti, A., Chasse, D., Joshi, M.-B., Harpole, D., Lancaster, J.M., Berchuck, A., Olson Jr, J.A., Marks, J.R., Dressman, H.K., West, M., Nevins, J.R.: Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439, 353–357 (2006)
Lee, E., Chuang, H.-Y., Kim, J.-W., Ideker, T., Lee, D.: Inferring Pathway Activity Toward Precise Disease Classification. PLoS. Comput. Biol. 4, e1000217 (2008)
Su, J., Yoon, B.-J., Dougherty, E.R.: Accurate and Reliable Cancer Classification Based On Probabilistic Inference of Pathway Activity. PLoS ONE 4 (2009)
Sootanan, P., Meechai, A., Prom-on, S., Chan, J.H.: Pathway Activity Inferences with Negatively Correlated Features for Pancreatic Cancer Classification. In: 2nd International Conference on BioMedical Engineering and Informatics (BMEI 2009), pp. 1888–1892. IEEE Press, China (2009)
Kim, K.-J., Cho, S.-B.: Ensemble Classifiers Based on Correlation Analysis for DNA Microarray Classification. Neurocomputing 70, 187–199 (2006)
Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J., Jatkoe, T., Berns, E.M.J.J., Atkins, D., Foekens, J.A.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005)
Edgar, R., Domrachev, M., Lash, A.E.: Gene Expression Omnibus: NCBI Gene Expression and Hybridization Array Data Repository. Nucl. Acids Res. 30, 207–210 (2002)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sootanan, P., Prom-on, S., Meechai, A., Chan, J.H. (2010). Microarray-Based Disease Classification Using Pathway Activities with Negatively Correlated Feature Sets. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-17534-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
Online ISBN: 978-3-642-17534-3
eBook Packages: Computer ScienceComputer Science (R0)