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.
[1] A. Schulze, and J. Downward, "Navigating gene expression using
microarrays - a technology review," Nature Cell Biology, Vol 3, pp.
E190-E195, Aug 2001.
[2] 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.
[3] P. D'haeseleer, S. Liang, and R. Somogyi, "Gene expression analysis and
genetic network modeling: Tutorial," Pacific Symposium on
Biocomputing (PSB ÔÇÿ99), 1999.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] G. Didier, P. Brezellec, E. Remy, and A. Henaut, "GeneANOVA - gene
expression analysis of variance," Bioinformatics, 18(3), pp. 490-491,
2002.
[11] T. J. Phillips, and J. K. Belknap, "Complex-trait genetics: emergence of
multivariate strategies," Nat. Rev. Neurosci., 3(6), pp. 478-485, 2002.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] E. Keedwell, and A. Narayanan, "Genetic algorithms for gene
expression analysis," EvoBio 2002, Springer Verlag LNCS 2611, pp.
76-86, 2003.
[18] J. Liu, H. Iba, and M. Ishizuka, "Selecting informative genes with
parallel genetic algorithms in tissue classification," Genome Informatics,
12, pp. 14-23, 2001.
[19] 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.
[20] J. M. Deutsch, "Evolutionary algorithms for finding optimal gene sets in
microarray prediction," Bioinformatics, 19, pp. 45-52, 2003.
[21] C. Creighton and S. Hanash, "Mining gene expression databases for
association rules," Bioinformatics, 19, pp. 79-86, 2003.
[22] 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.
[23] 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.
[24] 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.
[25] T. Akutsu, S. Miyano, and S. Kuhara, "Algorithms for inferring
qualitative models of biological networks," Pacific Symp. on
Biocomputing, 2000.
[26] 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.
[27] 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.
[28] H. Kargupta, "The gene expression messy genetic algorithm," Proc. Of
IEEE Intl. Conf. on Evolutionary Computation, 1996.
[29] 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.
[30] G. B. Fogel, and D. W. Corne (Editors), Evolutionary computation in
bioinformatics, Morgan Kaufmann, 2003.
[31] G. Kampis, "A Causal Model of Evolution," Proc. of 4th Asia-Pacific
Conf. on Simulated Evol. and Learning (SEAL 02), pp. 836-840, 2002.
[32] R. Dawkins, The blind watchmaker, Penguin Books, 1988.
[33] D. Stekel, Microarray bioinformatics, Cambridge University Press,
2003.
[34] R. Dawkins, The selfish gene, Oxford University Press, 1976.
[35] R. Dawkins, The extended phenotype, Oxford University Press, 1982.
[36] P. Baldi, and G. W. Hatfield, DNA microarrays and gene expression,
Cambridge University Press, 2002.
[37] S. Chu, J. DeRisi, M. Eisen, et al., "The transcriptional program of
sporulation in budding yeast," Science, 282, pp. 699-705, 1998.
[38] SGD project. "Saccharomyces Genome Database"
http://www.yeastgenome.org/ (15/9/2007).
[39] 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.
[1] A. Schulze, and J. Downward, "Navigating gene expression using
microarrays - a technology review," Nature Cell Biology, Vol 3, pp.
E190-E195, Aug 2001.
[2] 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.
[3] P. D'haeseleer, S. Liang, and R. Somogyi, "Gene expression analysis and
genetic network modeling: Tutorial," Pacific Symposium on
Biocomputing (PSB ÔÇÿ99), 1999.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] G. Didier, P. Brezellec, E. Remy, and A. Henaut, "GeneANOVA - gene
expression analysis of variance," Bioinformatics, 18(3), pp. 490-491,
2002.
[11] T. J. Phillips, and J. K. Belknap, "Complex-trait genetics: emergence of
multivariate strategies," Nat. Rev. Neurosci., 3(6), pp. 478-485, 2002.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] E. Keedwell, and A. Narayanan, "Genetic algorithms for gene
expression analysis," EvoBio 2002, Springer Verlag LNCS 2611, pp.
76-86, 2003.
[18] J. Liu, H. Iba, and M. Ishizuka, "Selecting informative genes with
parallel genetic algorithms in tissue classification," Genome Informatics,
12, pp. 14-23, 2001.
[19] 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.
[20] J. M. Deutsch, "Evolutionary algorithms for finding optimal gene sets in
microarray prediction," Bioinformatics, 19, pp. 45-52, 2003.
[21] C. Creighton and S. Hanash, "Mining gene expression databases for
association rules," Bioinformatics, 19, pp. 79-86, 2003.
[22] 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.
[23] 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.
[24] 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.
[25] T. Akutsu, S. Miyano, and S. Kuhara, "Algorithms for inferring
qualitative models of biological networks," Pacific Symp. on
Biocomputing, 2000.
[26] 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.
[27] 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.
[28] H. Kargupta, "The gene expression messy genetic algorithm," Proc. Of
IEEE Intl. Conf. on Evolutionary Computation, 1996.
[29] 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.
[30] G. B. Fogel, and D. W. Corne (Editors), Evolutionary computation in
bioinformatics, Morgan Kaufmann, 2003.
[31] G. Kampis, "A Causal Model of Evolution," Proc. of 4th Asia-Pacific
Conf. on Simulated Evol. and Learning (SEAL 02), pp. 836-840, 2002.
[32] R. Dawkins, The blind watchmaker, Penguin Books, 1988.
[33] D. Stekel, Microarray bioinformatics, Cambridge University Press,
2003.
[34] R. Dawkins, The selfish gene, Oxford University Press, 1976.
[35] R. Dawkins, The extended phenotype, Oxford University Press, 1982.
[36] P. Baldi, and G. W. Hatfield, DNA microarrays and gene expression,
Cambridge University Press, 2002.
[37] S. Chu, J. DeRisi, M. Eisen, et al., "The transcriptional program of
sporulation in budding yeast," Science, 282, pp. 699-705, 1998.
[38] SGD project. "Saccharomyces Genome Database"
http://www.yeastgenome.org/ (15/9/2007).
[39] 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.
@article{"International Journal of Information, Control and Computer Sciences:59242", author = "Rio G. L. D'Souza and K. Chandra Sekaran and A. Kandasamy", title = "A Phenomic Algorithm for Reconstruction of Gene Networks", abstract = "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.
", keywords = "Evolutionary computing, gene expression analysis, gene networks, microarray data analysis, phenomic algorithms.", volume = "1", number = "7", pages = "2128-6", }
