INFERENCE OF SMALL-WORLD NETWORKS IN MODEL ORGANISMS WITH THE SFFS-WS ALGORITHM.

Alisson Lucas de Souza; Fabricio Martins Lopes; Fábio Vicente

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Paper Title

INFERENCE OF SMALL-WORLD NETWORKS IN MODEL ORGANISMS WITH THE SFFS-WS ALGORITHM.

Authors

Alisson Lucas de Souza
Fabricio Martins Lopes
Fábio Vicente

Modality

Xpress presentation

Subject area

Systems Biology and Modeling

Publishing Date

26/04/2022

Country of Publishing

Brasil

Language of Publishing

Inglês

Paper Page

https://www.even3.com.br/anais/xmeetingxp2021/420093-inference-of-small-world-networks-in-model-organisms-with-the-sffs-ws-algorithm

ISBN

978-65-5941-645-5

Keywords

Gene Network Inference, Small World, Topology, System Biology

Summary

Gene Network Inference is a research area in Bioinformatics that has stood out in the study of Systems Biology and consists of understanding the complex network of interactions between different cellular components, such as DNA, mRNA, enzymes, proteins, activators, inhibitors, receptors, etc., who actively participate in the regulation of biological processes. When working with expression data, inferring gene networks becomes challenging, as the number of samples, in relation to the number of transcripts, is significantly smaller, which causes the "curse of dimensionality". Some research tries to deal with this problem by using, in addition to gene expression data, other biological information, which is generally available in public databases such as Gene Ontology, GenBank and KEGG, to assist in the gene network clustering process. Furthermore, in order to reduce the search space and improve inference, some works assume specific network topologies, such as the SFFS-BA algorithm, which uses the scale-free topology, and the SFFS-SW algorithm, which uses the small world topology.In this last algorithm, the small world topology was chosen because it offers a small medium minimum path and a high clustering coefficient, characteristics that were prioritized in similar networks presented by other authors in different approaches. Furthermore, the authors of this research applied the method to artificial data, through simulations in Artificial Gene Networks, to assess its efficiency. Thus, the main objective of this work is to improve the SFFS-SW algorithm and analyze its effectiveness in real data from E. coli (806 samples x 4511 transcripts), S. aureas (161 samples x 2810 transcripts) and S. cerevisiae ( 537 samples x 5950 transcripts), taken from the DREAM5 database.

Title of the Event: X-Meeting XPerience 2021
Title of the Proceedings of the event: X-Meeting presentations
Name of the Publisher: Even3
Means of Dissemination: Meio Digital

How to cite

SOUZA, Alisson Lucas de; LOPES, Fabricio Martins; VICENTE, Fábio. INFERENCE OF SMALL-WORLD NETWORKS IN MODEL ORGANISMS WITH THE SFFS-WS ALGORITHM... In: X-Meeting presentations. Anais...São Paulo(SP) AB3C, 2021. Available in: https//www.even3.com.br/anais/xmeetingxp2021/420093-INFERENCE-OF-SMALL-WORLD-NETWORKS-IN-MODEL-ORGANISMS-WITH-THE-SFFS-WS-ALGORITHM. Access in: 17/07/2025