GeneRank-based partly adaptive group-penalised multinomial regression for microarray classification
Authors: 
Juntao Li, Wenpeng DongAuthors Info & Claims
Pages 252 - 268
Abstract
This paper proposes a partly adaptive group-penalised multinomial regression for gene selection. Weights with biological significance are constructed by combing the gene expression information with gene ontology network via GeneRank. By introducing the weights into group lasso penalty, the partly adaptive group-penalised multinomial regression is proposed. Two algorithms for fitting the proposed model are presented on the base of blockwise descent. Experimental results on gene expression data of yeast diauxic shift demonstrate that the proposed method can select the stable genes and achieve the better classification performance.
References
[1]
Ashburner, M. (2000) 'Gene Ontology: tool for the unification of biology', Nature Genetics, Vol. 25, No. 1, pp. 25-29.
[2]
Baskaran, N., Kwoh, C.K. and Hui, K.M. (2013) 'Comprehensive detection of cancer gene expression profiles and gene networks are impacted by the choice of pre-processing algorithm and gene-selection method', International Journal of Data Mining and Bioinformatics, Vol. 7, No. 4, pp. 416-435.
[3]
Cawley, G.C. and Talbot, N.L.C. (2006) 'Gene selection in cancer classification using sparse logistic regression with Bayesian regularization', Bioinformatics, Vol. 22, No. 19, pp. 2348-2355.
[4]
Cognetti, D., Davis, D. and Sturtevant, J. (2002) 'The candida albicans 14-3-3 gene, bmh1, is essential for growth', Yeast, Vol. 19, No. 1, pp. 55-67.
[5]
Delphine, F., Kristiina, H., Gerda, C., Hilde, N., Tommaso Matteo, B., Steven, M., Gerrit T.S. B., Pia N., Sylvester A., Pedro R., Jose L. M., Dirk I. and Mieke V. L. (2007) 'The arabidopsis thaliana homolog of yeast bre1 has a function in cell cycle regulation during early leaf and root growth', Plant Cell, Vol. 19, No. 2, pp. 417-432.
[6]
Derisi, J. L., Iyer, V. R. and Brown, P. O. (1997) 'Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale', Science, Vol. 278, No. 5338, pp. 680686.
[7]
Draculic, T. and Dawes, I. C. (2000) 'A single glutaredoxin or thioredoxin gene is essential for viability in the yeast saccharomyces cerevisiae', Molecular Microbiology, Vol. 36, No. 5, pp. 1167-1174.
[8]
Du, W., Sun, Y., Wang, Y., Cao, Z., Zhang, C. and Liang, Y. (2013) 'A novel multi-stage feature selection method for microarray expression data analysis', International Journal of Data Mining and Bioinformatics, Vol. 7, No. 1, pp. 58-77.
[9]
Elia, L. and Marsh, L. (1996) 'Role of the abc transporter ste6 in cell fusion during yeast conjugation', Journal of Cell Biology, Vol. 135, No. 3, pp. 741-751.
[10]
Friedman, J., Hastie, T. and Tibshirani, R. (2010) 'Regularization paths for generalized linear models via coordinate descent', Journal of Statistical Software, Vol. 33, No. 1, pp. 1-22.
[11]
Han, B., Lai, H., Xie, R., Li, L. and Zhu, L. (2014) 'Identification of glioma cancer-alerted gene markers based on a diagnostic outcome correlation analysis preferential approach', International Journal of Data Mining and Bioinformatics, Vol. 9, No. 1, pp. 67-88.
[12]
Hasan, M.A., Hasan, M.K. and Mottalib, M.A. (2015) 'Linear regression-based feature selection for microarray data classification', International Journal of Data Mining and Bioinformatics, Vol. 11, No. 2, pp. 167-179.
[13]
Joseph, S. J., Robbins, K. R., Zhang, W. and Romdhane, R. (2010) 'Comparison of two output-coding strategies for multi-class tumor classification using gene expression data and latent variable model as binary classifier', Cancer Informatics, Vol. 9, No. 9, pp. 39-48.
[14]
Kim, H., Park, T. and Gelenbe, E. (2014) 'Identifying Disease Candidate Genes via Large-scale Gene Network Analysis', International Journal of Data Mining and Bioinformatics, Vol. 10, No. 2, pp. 175-188.
[15]
Lawrence, P., Brin, S., Motwami, R. and Winogarad, T. (1999) 'The PageRank Citation Ranking: Bringing Order to the Web', Stanford Infolab, Vol. 9, No. 1, pp. 1-14.
[16]
Li, J., Jia, Y. and Zhao, Z. (2013) 'Partly adaptive elastic net and its application to microarray classification and gene selection', Neural Computing and Applications, Vol. 22, No. 6, pp. 1193-1200.
[17]
Liu, Z. and Jiang, F. (2009) 'Gene identification and survival prediction with lp cox regression and novel similarity measure', International Journal of Data Mining and Bioinformatics, Vol. 3, No. 4, pp. 398-408.
[18]
Morrison, J., Breitling, R., Higham, D. and Gilbert, D. (2005) 'GeneRank: Using search engine technology for the analysis of microarry experiments', BMC Bioinformatics, Vol. 6, No. 1, pp. 233-246.
[19]
Puniya, B.L., Kulshreshtha, D. and Verma, S.P. (2013) 'Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets', Molecular Biosystems, Vol. 9, No. 11, pp. 2798-2815.
[20]
Rowley, A., Singer, R. A. and Johnston, G. C. (1991) 'Cdc68, a yeast gene that affects regulation of cell proliferation and transcription, encodes a protein with a highly acidic carboxyl terminus', Molecular and Cellular Biology, Vol. 11, No. 11, pp. 5718-5726.
[21]
Shin, M. and Lee, H. (2011) 'Prioritizing candidate genes by weighted network structure for the identification of disease marker gene', Biochip Journal, Vol. 5, No.1, pp. 27-31.
[22]
Simon, N., Friedman, J. and Hastie, T. (2013a) 'A blockwise descent algorithm for group-penalized multiresponse and multinomial regression', Journal of Statistical Software, Vol. 10, No. 2, pp. 1-11.
[23]
Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2013b) 'A sparse-group lasso', Journal of Computational and Graphical Statistics, Vol. 22, No. 2, pp. 231-245.
[24]
Vincent, M. and Hansen, N.R. (2014) 'Sparse group lasso and high dimensional multinomial classification', Computational Statistics and Data Analysis, Vol. 71, No. 1, pp. 771-786.
[25]
Wu, G., Zhang, Y. and Wei, Y. (2010) 'Krylov Subspace Algorithms for Computing GeneRank for the Analysis of Microarray Data Mining', Journal of Computational Biology, Vol. 17, No. 4, pp. 631-646.
[26]
Yuan, M. and Lin, Y. (2006) 'Model selection and estimation in regression with grouped variables', Journal of the Royal Statistical Society, Series B, Vol. 68, No. 1, pp. 49-67.
[27]
Zhang, J., Xiang, Y., Wang, Y., Zhou, W., Xiang, Y. and Guan, Y. (2013) 'Network traffic classification using correlation information', IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 1, pp. 104-117.
[28]
Zou, H. (2006) 'The adaptive lasso and its oracle properties', Journal of the American Statistical Association, Vol. 101, No. 476, pp. 1418-1429.
[29]
Zou, L., Mitchell, J. and Stillman, B. (2015) 'Cdc45, a novel yeast gene that functions with the origin recognition complex and MCM proteins in initiation of DNA replication', Trends in Anaesthesia and Critical Care, Vol. 5, No. 1, pp. 42-47.
- GeneRank-based partly adaptive group-penalised multinomial regression for microarray classification
Recommendations
Gene selection using independent variable group analysis for tumor classification
Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. So, gene expression data from DNA microarray are characterized by many measured variables (genes) on only a few samples. One important application of gene ...
Gene identification and survival prediction with L<SUB align=right>p Cox regression and novel similarity measure
In this paper, Cox's proportional hazards model with L<SUB align=right>p penalty method is developed for simultaneous gene selection and survival prediction. L<SUB align=right>p penalty shrinks coefficients and produces some coefficients ...
Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest
A significant amount of microarray gene expression data is available on the Internet, and researchers are allowed to analyze such data freely. However, microarray data includes thousands of genes, and analysis using conventional techniques is too ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
Inderscience Publishers
Geneva 15, Switzerland
Publication History
Published: 01 January 2016
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 11 Dec 2024
Other Metrics
Citations
View Options
View options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in