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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Extracting Gradual Rules to Reveal Regulation Between Genes

Author(s): Manel Gouider*, Ines Hamdi and Henda Ben Ghezala

Volume 16, Issue 3, 2021

Published on: 11 July, 2020

Page: [395 - 405] Pages: 11

DOI: 10.2174/1574893615999200711170945

Price: $65

Abstract

Background: Gene regulation represents a very complex mechanism in the cell initiated to increase or decrease gene expression. This regulation of genes forms a Gene regulatory Network GRN composed of a collection of genes and products of genes in interaction. The high throughput technologies that generate a huge volume of gene expression data are useful for analyzing the GRN. The biologists are interested in the relevant genetic knowledge hidden in these data sources. Although, the knowledge extracted by the different data mining approaches of the literature is insufficient for inferring the GRN topology or does not give a good representation of the real genetic regulation in the cell.

Objective: In this work, we performed the extraction of genetic interactions from the high throughput technologies, such as the microarrays or DNA chips.

Methods: In this paper, in order to extract expressive and explicit knowledge about the interactions between genes, we used the method of gradual patterns and rules extraction applied on numerical data that extracts the frequent co-variations between gene expression values. Furthermore, we choose to integrate experimental biological data and biological knowledge in the process of knowledge extraction of genetic interactions.

Results: The validation results on real gene expression data of the model plant Arabidopsis and human lung cancer showed the performance of this approach.

Conclusion: The extracted gradual rules express the genetic interactions composed of a GRN. These rules help to understand complex systems and cellular functions.

Keywords: Knowledge extraction, gene expression data, gradual rules, GRN, genetic interaction, arabidopsis, floral development, lung cancer.

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Graphical Abstract

[1]
Gouider M, Hamdi I, Ghezala H. Mining gene expression data: patterns extraction for gene regulatory networks. Intell Syst Des Appl. 2018; 74-82..
[2]
Ulitsky I, Maron-Katz A, Shavit S, et al. Expander: from expression microarrays to networks and functions. Nat Protoc 2010; 5(2): 303-22.
[http://dx.doi.org/10.1038/nprot.2009.230] [PMID: 20134430]
[3]
Kaderali L, Radde N. Inferring gene regulatory networks from expression data computational intelligence in bioinformatics. Berlin, Heidelberg: Springer 2008; pp. 33-74.
[4]
Dussaut JS, Gallo CA, Cravero F, Martínez MJ, Carballido JA, Ponzoni I. GeRNet: a gene regulatory network tool. Biosystems 2017; 162: 1-11.
[http://dx.doi.org/10.1016/j.biosystems.2017.08.006] [PMID: 28860069]
[5]
Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACMSIGMOD International Conference on Management of Data. 1993; pp. 207-16..
[http://dx.doi.org/10.1145/170035.170072]
[6]
Salle P, Sandra B, Maguelonne T. Motifs Séquentiels Discriminants pour les puces ADN . InforSID: Informatique des organisations et Systèmes d’Information et de Décision 2009; pp. 397-412..
[7]
Choong Y, Di J, Laurent A, Laurent D, Teisseire M. Classification based on gradual patterns. Proceedings of the Int Conf on Soft Computing and Pattern Recognition. 2009; pp. 7-12..
[8]
Coen ES, Meyerowitz EM, Meyerowit Elliot M. The war of the whorls: genetic interactions controlling flower development. Nature 1991; 353(6339): 31-7.
[http://dx.doi.org/10.1038/353031a0] [PMID: 1715520]
[9]
Donald Fosket E. Plant growth and development: a molecular approach. Academic Press 1994; pp. 498-509.
[10]
Vachon G, Tichtinsky G, Parcy F. LEAFY, le régulateur clé du développement de la fleur. Biol Aujourdhui 2012; 206(1): 63-7.
[http://dx.doi.org/10.1051/jbio/2012006] [PMID: 22463997]
[11]
HAMÈS C. Etude fonctionnelle et structurale du régulateur floral LEAFY d’Arabidpsis thaliana. PhD Thesis . 2008.
[12]
Schmid M, Davison TS, Henz SR, et al. A gene expression map of Arabidopsis thaliana development. Nat Genet 2005; 37(5): 501-6.
[http://dx.doi.org/10.1038/ng1543] [PMID: 15806101]
[13]
Wu Z, Irizarry RA, Gentleman R, Martinez-Murillo F. A model-based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc 2004; 99(468): 909-17.
[http://dx.doi.org/10.1198/016214504000000683]
[14]
Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43(7)e47
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[15]
Wang S, Huang H, Han R, et al. BpAP1 directly regulates BpDEF to promote male inflorescence formation in Betula platyphylla × B. pendula. Tree Physiol 2019; 39(6): 1046-60.
[http://dx.doi.org/10.1093/treephys/tpz021] [PMID: 30976801]
[16]
Negrevergne B, Termier A, Rousset MC, Méhaut JF. Paraminer: a generic pattern mining algorithm for multi-core architectures. Data Min Knowl Discov 2014; 28(3): 593-633.
[http://dx.doi.org/10.1007/s10618-013-0313-2]
[17]
Alexa A, Rahnenführer J. Gene set enrichment analysis with topGO. Bioconductor Improv 2009; p. 27.
[18]
Tan G, Lenhard B. TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 2016; 32(10): 1555-6.
[http://dx.doi.org/10.1093/bioinformatics/btw024] [PMID: 26794315]
[19]
Stearman RS, Dwyer-Nield L, Zerbe L, et al. Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol 2005; 167(6): 1763-75.
[http://dx.doi.org/10.1016/S0002-9440(10)61257-6] [PMID: 16314486]

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