Comparing gene regulatory inferring algorithms with different perspective

Comparing gene regulatory inferring algorithms with different perspective

Shaimaa M. ElembabyVidan F. Ghoneim Manal Abdel Wahed 

Faculty of engineering, Cairo University, Giza, Egypt

Faculty of engineering, Helwan University, Cairo, Egypt

Corresponding Author Email:
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More than hundred algorithms were developed to infer Gene Regulatory Networks (GRN) describing relations between genes. GRN construction has been a field of interest to researchers since the beginning of the current century. Many competitions were held to encourage the development of GRN inference algorithms, such competitions offer synthetic data to enable the validation of proposed algorithms. A GRN is constructed from an adjacency matrix which contains relations between genes. The developers of many of the GRN inference algorithms set a threshold on the adjacency matrix to construct GRN based on high gene-gene relation weights. This threshold strategy was followed in previous studies to increase the accuracy of any algorithm but yet based on no well-known rule. A different perspective here is to compare different GRN inference algorithms without setting any threshold. Comparison in this work is among different GRN inference algorithms by implementing all algorithms with no threshold on values of adjacency matrices: Differential Equation methods (TSNI), Granger Causality, GP4GRN, GENIE3, NIMEFI (SVR), and PLSNET. Another comparison between different distance metric equations to create adjacency matrix is also studied in an attempt to construct GRN. GP4GRN and GENIE3 participate in producing best results for dream4 InSilico_Size10 while GENIE3 produce best results for all networks of dream4 InSilico_Size100.


gene regulatory network, adjacency matrix, distance metrics

1. Introduction
2. Materials and methods
3. Results and discussions
4. Conclusion

Äijö T., Lähdesmäki H. (2009). Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics, Vol. 25, No. 22, pp. 2937-2944.

Akutsu T., Kuhara S., Maruyama O., Miyano S. (1998). A system for identifying genetic networks from gene expression patterns produced by gene disruptions and over expressions. Universal Academy Press.

Bansal M., Gatta G. D., Bernardo D. (2006). Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics, Vol. 22, No. 7, p. 815-822.

Bonneau R., Reiss D. J., Shannon P., Facciotti M., Hood L., Baliga N., Thorsson V. (2006). The Inferelator: An algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol., Vol. 7, No. 5, pp. R36.

Deza E., Deza M. M. (2009). Encyclopedia of distances. Springer, pp. 94.

Feizi S., Marbach D., Médard M., Kellis M. (2013). Network deconvolution as a general method to distinguish direct dependencies in networks. Nature Biotechnology, Vol. 31, No. pp. 8.

Granger C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, Vol. 37, No. 3, pp. 424-438.

Guo S., Jiang Q., Chen L., Guo D. (2016). Gene regulatory network inference using PLS-based methods. BMC Bioinformatics, Vol. 17, No. 1, pp. 545.

Huynh-Thu V. A., Irrthum A., Wehenkel L., Geurts P. (2010). Inferring regulatory networks from expression data using tree-based methods. PLoS One, Vol. 5, No. 9. pp. e12776.

Jing L., Michael K. Ng, Liu Y. (2010). Construction of gene networks with hybrid approach from expression profile and gene ontology. IEEE Transactions On Information Technology In Biomedicine, Vol. 14, No. 1, pp. 107-118.

Kentzoglanakis K., Poole M. (2012). A swarm intelligence framework for reconstructing gene networks: Searching for biologically plausible architectures. IEEE/ACM Transactions On Computational Biology And Bioinformatics, Vol. 9, No. 2, pp. 358-371.

Kesavan E., Gowthaman N., Tharani S., Manoharan S., Arunkumar E. (2016). Design and implementation of internal model control and particle swarm optimization based PID for heat exchanger system. International Journal of Heat and Technology, Vol. 34, No. 3, pp. 386-390.

Küffner R., Petri T., Tavakkolkhah P., Windhager L., Zimmer R. (2012). Inferring gene regulatory networks by ANOVA. Bioinformatics, Vol. 28, No. 10, pp. 1376-1382.

Lian R., Zhou C., Goertzel B. (2017). Probabilistic rank correlation - a new rank and comparison based correlation coefficient with a simple. Pragmatic Transitivity Condition. AMSE Journals, IIETA publications, Advances A, Vol. 54, No. 4, pp. 476-496.

Liang C. H., Zeng S., Li Z. X., Yang D. G., Sherif S. A. (2016). Optimal design of plate-fin heat sink under natural convection using a particle swarm optimization algorithm. International Journal of Heat and Technology, Vol. 34, No. 2, pp. 275-280.

McCune B., Grace J. B. (2002). Analysis of Ecological Communities. MjM Software, Gleneden Beach, Oregon, USA. ISBN: 0-9721290-0-6

Meyer P. E., Lafitte F., Bontempi G. (2008). MiNET: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics, Vol. 9, pp. 461.

Reverter A., Chan E. K. F. (2008). Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics, Vol. 24, No. 21, pp. 2491-2497.

Ruyssinck J., Geurts P., Dhaene T., Huynh-Thu V. A., Demeester P., Saeys Y. (2014). Nimefi: gene regulatory network inference using multiple ensemble feature importance algorithms. PLoS One, Vol. 9, No. 3, pp. e92709.

Seth A. K. (2010). A MATLAB toolbox for Granger causal connectivity analysis. Elsevier, Journal of Neuroscience Methods, Vol. 186, No. 2, pp. 262-273.

Sławek J., Arodź T. (2013). ENNET: inferring large gene regulatory networks from expression data using gradient boosting. BMC Syst Biol., Vol. 7, No. 1, pp. 106.

Székely G. J., Rizzo M. L., Bakirov N. K. (2007). Measuring and testing dependence by correlation of distances. Ann. Statist., Vol. 35, No. 6, pp. 2313-2817.

Thomas R. (1991). Regulatory networks seen as asynchronous automata: A logical description. Journal of Theoretical Biolgy, Vol. 153, pp. 1-23.

Yghoobi H., Haghipour S., Hamzeiy H., Asadi- Khiavi M. (2012). A review of modelling techniques for genetic regulatory networks. Journal of Medical Signals and sensors, Vol. 2, No. 1, pp. 61-70.