Gene networks inference from expression data using a recurrent neuro-fuzzy approach

I. Maraziotis, A. Dragomir, A. Bezerianos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The reverse engineering paradigm is given increasing attention in computational molecular biology lately. One of the goals is to understand how gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions. We present an approach for inferring the complex causal relationships among genes from microarray experimental data based on a recurrent neuro-fuzzy method. The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account dynamical aspects of genes regulation through its recurrent structure. We tested our approach on a set of genes known to be highly regulated during the yeast cell-cycle. The retrieved gene interactions correspond to the ones validated by previous biological studies, while our method surpasses previous computational techniques that attempted gene networks reconstruction, being able to retrieve significantly more biologically valid relationships among genes. At the same time, our method is able to predict time series for the expression of the genes based on the information extracted from a training subset of the data. The results prove highly accurate prediction capability.

Original languageEnglish (US)
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages4834-4837
Number of pages4
Volume7 VOLS
StatePublished - 2005
Externally publishedYes
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Other

Other2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
CountryChina
CityShanghai
Period9/1/059/4/05

Fingerprint

Genes
Molecular biology
Reverse engineering
Fuzzy rules
Microarrays
Gene expression
Yeast
Large scale systems
Time series
Cells
Molecules
Proteins

ASJC Scopus subject areas

  • Bioengineering

Cite this

Maraziotis, I., Dragomir, A., & Bezerianos, A. (2005). Gene networks inference from expression data using a recurrent neuro-fuzzy approach. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (Vol. 7 VOLS, pp. 4834-4837). [1615554]

Gene networks inference from expression data using a recurrent neuro-fuzzy approach. / Maraziotis, I.; Dragomir, A.; Bezerianos, A.

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Vol. 7 VOLS 2005. p. 4834-4837 1615554.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Maraziotis, I, Dragomir, A & Bezerianos, A 2005, Gene networks inference from expression data using a recurrent neuro-fuzzy approach. in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. vol. 7 VOLS, 1615554, pp. 4834-4837, 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, Shanghai, China, 9/1/05.
Maraziotis I, Dragomir A, Bezerianos A. Gene networks inference from expression data using a recurrent neuro-fuzzy approach. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Vol. 7 VOLS. 2005. p. 4834-4837. 1615554
Maraziotis, I. ; Dragomir, A. ; Bezerianos, A. / Gene networks inference from expression data using a recurrent neuro-fuzzy approach. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Vol. 7 VOLS 2005. pp. 4834-4837
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