Abstract
Molecular profiling technologies monitor thousands of transcripts, proteins, metabolites or other species concurrently in biological samples of interest. Given two-class, high-dimensional profiling data, nominal Liknon [4] is a specific implementation of a methodology for performing simultaneous relevant feature identification and classification. It exploits the well-known property that minimizing an l 1 norm (via linear programming) yields a sparse hyperplane [15],[26],[2],[8],[17]. This work (i) examines computational, software and practical issues required to realize nominal Liknon, (ii) summarizes results from its application to five real world data sets, (iii) outlines heuristic solutions to problems posed by domain experts when interpreting the results and (iv) defines some future directions of the research.
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
S.V. Allander, N.N. Nupponen, M. Ringner, G. Hostetter, G.W. Maher, N. Goldberger, Y. Chen, Carpten J., A.G. Elkahloun, and P.S. Meltzer. Gastrointestinal Stromal Tumors with KIT mutations exhibit a remarkably homogeneous gene expression profile. Cancer Research, 61:8624–8628, 2001.
K. Bennett and A. Demiriz. Semi-supervised support vector machines. In Neural and Information Processing Systems, volume 11. MIT Press, Cambridge MA, 1999.
A. Bhattacharjee, W.G. Richards, J. Staunton, C. Li, S. Monti, P. Vasa, C. Ladd, J. Beheshti, R. Bueno, M. Gillette, M. Loda, G. Weber, E.J. Mark, E.S. Lander, W. Wong, B.E. Johnson, T.R. Golub, D.J. Sugarbaker, and M. Meyerson. Classification of human lung carcinomas by mrna expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci., 98:13790–13795, 2001.
C. Bhattacharyya, L.R. Grate, A. Rizki, D.C. Radisky, F.J. Molina, M.I. Jordan, M.J. Bissell, and I.S. Mian. Simultaneous relevant feature identification and classification in high-dimensional spaces: application to molecular profiling data. Submitted, Signal Processing, 2002.
M.P. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares, Jr, and D. Haussler. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci., 97:262–267, 2000.
P. Cheeseman and J. Stutz. Bayesian Classification (AutoClass): Theory and Results. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 153–180. AAAI Press/MIT Press, 1995. The software is available at the URL http://www.gnu.org/directory/autoclass.html.
M.L. Chow, E.J. Moler, and I.S. Mian. Identifying marker genes in transcription profile data using a mixture of feature relevance experts. Physiological Genomics, 5:99–111, 2001.
N. Cristianini and J. Shawe-Taylor. Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge, England, 2000.
S.M. Dhanasekaran, T.R. Barrette, R. Ghosh, D. Shah, S. Varambally, K. Kurachi, K.J. Pienta, M.J. Rubin, and A.M. Chinnaiyan. Delineation of prognostic biomarkers in prostate cancer. Nature, 432, 2001.
D.L. Donoho and X. Huo. Uncertainty principles and idea atomic decomposition. Technical Report, Statistics Department, Stanford University, 1999.
R. Fletcher. Practical Methods in Optimization. John Wiley & Sons, New York, 2000.
T. Furey, N. Cristianini, N. Duffy, D. Bednarski, M. Schummer, and D. Haussler. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16:906–914, 2000.
M.E. Garber, O.G. Troyanskaya, K. Schluens, S. Petersen, Z. Thaesler, M. Pacyana-Gengelbach, M. van de Rijn, G.D. Rosen, C.M. Perou, R.I. Whyte, R.B. Altman, P.O. Brown, D. Botstein, and I. Petersen. Diversity of gene expression in adenocarcinoma of the lung. Proc. Natl. Acad. Sci., 98:13784–13789, 2001.
T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfeld, and E.S. Lander. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:531–537, 1999. The data are available at the URL http://waldo.wi.mit.edu/MPR/data_sets.html.
T. Graepel, B. Herbrich, R. Schölkopf, A.J. Smola, P. Bartlett, K. Müller, K. Obermayer, and R.C. Williamson. Classification on proximity data with lp-machines. In Ninth International Conference on Artificial Neural Networks, volume 470, pages 304–309. IEE, London, 1999.
L.R. Grate, C. Bhattacharyya, M.I. Jordan, and I.S. Mian. Integrated analysis of transcript profiling and protein sequence data. In press, Mechanisms of Ageing and Development, 2002.
T. Hastie, R. Tibshirani, and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York, 2000.
I. Hedenfalk, D. Duggan, Y. Chen, M. Radmacher, M. Bittner, R. Simon, P. Meltzer, B. Gusterson, M. Esteller, M. Raffeld, Z. Yakhini, A. Ben-Dor, E. Dougherty, J. Kononen, L. Bubendorf, W. Fehrle, S. Pittaluga, S. Gruvberger, N. Loman, O. Johannsson, H. Olsson, B. Wilfond, G. Sauter, O.-P. Kallioniemi, A. Borg, and J. Trent. Gene-expression profiles in hereditary breast cancer. New England Journal of Medicine, 344:539–548, 2001.
J. Khan, J.S. Wei, M. Ringner, L.H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, Antonescu C.R., Peterson C., and P.S. Meltzer. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 7:673–679, 2001.
G. Lanckerit, L. El Ghaoui, C. Bhattacharyya, and M.I. Jordan. Minimax probability machine. Advances in Neural Processing systems, 14, 2001.
L.A. Liotta, E.C. Kohn, and E.F. Perticoin. Clinical proteomics. personalized molecular medicine. JAMA, 14:2211–2214, 2001.
E.J. Moler, M.L. Chow, and I.S. Mian. Analysis of molecular profile data using generative and discriminative methods. Physiological Genomics, 4:109–126, 2000.
D.A. Notterman, U. Alon, A.J. Sierk, and A.J. Levine. Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. Cancer Research, 61:3124–3130, 2001.
E.F. Petricoin III, A.M. Ardekani, B.A. Hitt, P.J. Levine, V.A. Fusaro, S.M. Steinberg, G.B Mills, C. Simone, D.A. Fishman, E.C. Kohn, and L.A. Liotta. Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 359:572–577, 2002.
S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mesirov, T. Poggio, W. Gerald, M. Loda, E.S. Lander, and T.R. Golub. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci., 98:15149–15154, 2001. The data are available from http://www-genome.wi.mit.edu/mpr/GCM.html.
A. Smola, T.T. Friess, and B. Schölkopf. Semiparametric support vector and linear programming machines. In Neural and Information Processing Systems, volume 11. MIT Press, Cambridge MA, 1999.
T. Sorlie, C.M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey, T. Thorsen, H. Quist, J.C. Matese, P.O. Brown, D. Botstein, P.E. Lonning, and A.-L. Borresen-Dale. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci., 98:10869–10874, 2001.
A.I. Su, J.B. Welsh, L.M. Sapinoso, S.G. Kern, P. Dimitrov, H. Lapp, P.G. Schultz, S.M. Powell, C.A. Moskaluk, H.F. Frierson Jr, and G.M. Hampton. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Research, 61:7388–7393, 2001.
L.J. van’t Veer, H. Dai, M.J. van de Vijver, Y.D. He, A.A. Hart, M. Mao, H.L. Peterse, van der Kooy K., M.J. Marton, A.T. Witteveen, G.J. Schreiber, R.M. Kerkhoven, C. Roberts, P.S. Linsley, R. Bernards, and S.H. Friend. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415:530–536, 2002.
V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
J.B. Welsh, L.M. Sapinoso, A.I. Su, S.G. Kern, J. Wang-Rodriguez, C.A. Moskaluk, J.F. Frierson Jr, and G.M. Hampton. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Research, 61:5974–5978, 2001.
J. Weston, Mukherjee S., O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature Selection for SVMs. In Advances in Neural Information Processing Systems, volume 13, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grate, L.R., Bhattacharyya, C., Jordan, M.I., Mian, I.S. (2002). Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. In: Guigó, R., Gusfield, D. (eds) Algorithms in Bioinformatics. WABI 2002. Lecture Notes in Computer Science, vol 2452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45784-4_1
Download citation
DOI: https://doi.org/10.1007/3-540-45784-4_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44211-0
Online ISBN: 978-3-540-45784-8
eBook Packages: Springer Book Archive