A Phenomic Algorithm for Reconstruction of Gene Networks
The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074757Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1574
 A. Schulze, and J. Downward, "Navigating gene expression using microarrays - a technology review," Nature Cell Biology, Vol 3, pp. E190-E195, Aug 2001.
 L. A. Soinov, M. A. Krestyaninova, and A. Brazma, "Towards reconstruction of gene networks from expression data by supervised learning," Genome Biology, 4(1), pp. R6, 2003.
 P. D'haeseleer, S. Liang, and R. Somogyi, "Gene expression analysis and genetic network modeling: Tutorial," Pacific Symposium on Biocomputing (PSB ÔÇÿ99), 1999.
 W. P. Kuo, E. Kim, J. Trimarchi J, et al., "A primer on gene expression and microarrays for machine learning researchers," Jour. of Biomedical Informatics, 37 (2004), pp. 293-303, 2004.
 N. L. W. van Hal, O. Vorst, A. M. M. L. van Houwelingen, et al., "The application of DNA microarrays in gene expression analysis," Jour. of Biotechnology, 78, pp. 271-280, 2000.
 M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, "Quantitative monitoring of gene expression patterns with complementary DNA microarray," Science, 270 (5235), pp. 467-470, 1995.
 W. P. Kuo, E. Mendez, C. Chen, et al., "Functional relationships between gene pairs in oral squamous cell carcinoma," Proc. AMIA Symp. 2003, pp. 371-375, 2003.
 O. G. Troyanskaya, M. E. Garber, P. O. Brown, D. Botstein, and R. B. Altman, "Nonparametric methods for identifying differentially expressed genes in microarray data," Bioinformatics, 18(11), pp. 1454-1461, 2002.
 K. A. Baggerly, K. R. Coombes, K. R. Hess, D. N. Stivers, L. V. Abruzzo, and W. Zhang, "Identifying differentially expressed genes in cDNA microarray experiments," Jour. Comput. Biol., 8(6), pp. 639-659, 2001.
 G. Didier, P. Brezellec, E. Remy, and A. Henaut, "GeneANOVA - gene expression analysis of variance," Bioinformatics, 18(3), pp. 490-491, 2002.
 T. J. Phillips, and J. K. Belknap, "Complex-trait genetics: emergence of multivariate strategies," Nat. Rev. Neurosci., 3(6), pp. 478-485, 2002.
 S. Ramaswamy, P. Tamayo, et al., "Multiclass cancer diagnosis using tumor gene expression signatures," Proc. Natl. Acad. Sci. USA, 98(26), pp. 15149-15154, 2001.
 J. Nikkila, P. Toronen, S. Kaski, J. Venna, E. Castren, and G. Wong, "Analysis and visualization of gene expression data using selforganizing maps," Neural Netw., 15(8-9), pp. 953-966, 2002.
 G. N. Fuller, K. R. Hess, C. H. Rhee, et al., "Molecular classification of human diffuse gliomas by multidimensional scaling analysis of gene expression profiles parallels morphology-based classification, correlates with survival, and reveals clinically relevant novel glioma subsets," Brain Pathol., 12(1), pp. 108-116, 2002.
 M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, "Cluster analysis and display of genome-wide expression patterns," Proc. Natl. Acad. Sci. USA, 95(25), pp. 14863-14868, 1998.
 T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioinformatics, 16(10), pp. 906-914, 2000.
 E. Keedwell, and A. Narayanan, "Genetic algorithms for gene expression analysis," EvoBio 2002, Springer Verlag LNCS 2611, pp. 76-86, 2003.
 J. Liu, H. Iba, and M. Ishizuka, "Selecting informative genes with parallel genetic algorithms in tissue classification," Genome Informatics, 12, pp. 14-23, 2001.
 C. H. Ooi, and P. Tan, "Genetic algorithms applied to multi-class prediction for the analysis of gene expression data," Bioinformatics, 19, pp. 37-44, 2003.
 J. M. Deutsch, "Evolutionary algorithms for finding optimal gene sets in microarray prediction," Bioinformatics, 19, pp. 45-52, 2003.
 C. Creighton and S. Hanash, "Mining gene expression databases for association rules," Bioinformatics, 19, pp. 79-86, 2003.
 R. Somogyi, S. Fuhrman, M. Askenazi, and A. Wuensche, "The gene expression matrix: towards the extraction of genetic network architectures," Proc. of Second World Cong. of Nonlinear Analysts (WCNA96), 30(3), pp. 1815-1824, 1997.
 S. Liang, S. Fuhrman, and R. Somogyi, "REVEAL, a general reverse engineering algorithm for inference of genetic network architectures," Pacific Symp. on Biocomputing, 3, pp. 18-29, 1998.
 T. Akutsu, S. Miyano, and S. Kuhara, "Identification of genetic networks from a small number of gene expression patterns under the boolean network model," Pacific Symp. on Biocomputing, 4, pp. 17-28, 1999.
 T. Akutsu, S. Miyano, and S. Kuhara, "Algorithms for inferring qualitative models of biological networks," Pacific Symp. on Biocomputing, 2000.
 P. D'haeseleer, S. Liang, and R. Somogyi, "Genetic network inference: from co-expression clustering to reverse engineering," Bioinformatics, 16(8), pp. 707-726, 2000.
 D. Jiang, C. Tang, and A. Zhang, "Cluster analysis for gene expression data: a survey," IEEE Trans. on Knowledge and Data Engg., Vol 16, No 11, pp. 1370-1386, 2004.
 H. Kargupta, "The gene expression messy genetic algorithm," Proc. Of IEEE Intl. Conf. on Evolutionary Computation, 1996.
 J. Falqueto, J. M. Barreto, and P. S. da Silva Borges, "Amplification of perspectives in the use of evolutionary computation," BIBE 2000, pp. 150, IEEE Int-l. Symp. on Bioinformatics and Biomedical Engg., 2000.
 G. B. Fogel, and D. W. Corne (Editors), Evolutionary computation in bioinformatics, Morgan Kaufmann, 2003.
 G. Kampis, "A Causal Model of Evolution," Proc. of 4th Asia-Pacific Conf. on Simulated Evol. and Learning (SEAL 02), pp. 836-840, 2002.
 R. Dawkins, The blind watchmaker, Penguin Books, 1988.
 D. Stekel, Microarray bioinformatics, Cambridge University Press, 2003.
 R. Dawkins, The selfish gene, Oxford University Press, 1976.
 R. Dawkins, The extended phenotype, Oxford University Press, 1982.
 P. Baldi, and G. W. Hatfield, DNA microarrays and gene expression, Cambridge University Press, 2002.
 S. Chu, J. DeRisi, M. Eisen, et al., "The transcriptional program of sporulation in budding yeast," Science, 282, pp. 699-705, 1998.
 SGD project. "Saccharomyces Genome Database" http://www.yeastgenome.org/ (15/9/2007).
 Z. Lubovac, and B. Olsson, "Towards reverse engineering of genetic regulatory networks," Technical Report No. HS-IDA-TR-03-003, University of Skovde, Sweden, 2003.