Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions

Ioannis Maraziotis, Andrei Dragomir, Anastasios Bezerianos

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

Abstract

Understanding the way gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions is currently one of the central goals in computational molecular biology. We propose 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. The gene interactions retrieved from a set of genes known to be highly regulated during the yeast cell-cycle are validated by 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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages24-34
Number of pages11
Volume3695 LNBI
DOIs
StatePublished - 2005
Externally publishedYes
Event1st International Symposium on Computational Life Sciences, CompLife 2005 - Konstanz, Germany
Duration: Sep 25 2005Sep 27 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3695 LNBI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Symposium on Computational Life Sciences, CompLife 2005
CountryGermany
CityKonstanz
Period9/25/059/27/05

Fingerprint

Reverse engineering
Neuro-fuzzy
Reverse Engineering
Regulator Genes
Fuzzy Model
Network Model
Genes
Gene
Interaction
Gene Regulatory Networks
Computational Molecular Biology
Gene Networks
Gene Regulation
Computational Techniques
Gene Regulatory Network
Cell Cycle
Molecular biology
Microarray Data
Fuzzy Rules
Computational Biology

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Maraziotis, I., Dragomir, A., & Bezerianos, A. (2005). Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3695 LNBI, pp. 24-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3695 LNBI). https://doi.org/10.1007/11560500_3

Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions. / Maraziotis, Ioannis; Dragomir, Andrei; Bezerianos, Anastasios.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3695 LNBI 2005. p. 24-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3695 LNBI).

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

Maraziotis, I, Dragomir, A & Bezerianos, A 2005, Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3695 LNBI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3695 LNBI, pp. 24-34, 1st International Symposium on Computational Life Sciences, CompLife 2005, Konstanz, Germany, 9/25/05. https://doi.org/10.1007/11560500_3
Maraziotis I, Dragomir A, Bezerianos A. Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3695 LNBI. 2005. p. 24-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11560500_3
Maraziotis, Ioannis ; Dragomir, Andrei ; Bezerianos, Anastasios. / Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3695 LNBI 2005. pp. 24-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{bbd41f3986c042ca9a3351c4ad971295,
title = "Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions",
abstract = "Understanding the way gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions is currently one of the central goals in computational molecular biology. We propose 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. The gene interactions retrieved from a set of genes known to be highly regulated during the yeast cell-cycle are validated by 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.",
author = "Ioannis Maraziotis and Andrei Dragomir and Anastasios Bezerianos",
year = "2005",
doi = "10.1007/11560500_3",
language = "English (US)",
isbn = "3540291040",
volume = "3695 LNBI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "24--34",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Recurrent neuro-fuzzy network models for reverse engineering gene regulatory interactions

AU - Maraziotis, Ioannis

AU - Dragomir, Andrei

AU - Bezerianos, Anastasios

PY - 2005

Y1 - 2005

N2 - Understanding the way gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions is currently one of the central goals in computational molecular biology. We propose 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. The gene interactions retrieved from a set of genes known to be highly regulated during the yeast cell-cycle are validated by 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.

AB - Understanding the way gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions is currently one of the central goals in computational molecular biology. We propose 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. The gene interactions retrieved from a set of genes known to be highly regulated during the yeast cell-cycle are validated by 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.

UR - http://www.scopus.com/inward/record.url?scp=33646201924&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646201924&partnerID=8YFLogxK

U2 - 10.1007/11560500_3

DO - 10.1007/11560500_3

M3 - Conference contribution

AN - SCOPUS:33646201924

SN - 3540291040

SN - 9783540291046

VL - 3695 LNBI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 24

EP - 34

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -