Žiga Pušnik (Author), Miha Mraz (Author), Nikolaj Zimic (Author), Miha Moškon (Author)

Abstract

Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Informationbased Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.

Keywords

inferenca Booleovih mrež;gensko regulatorna omrežja;statična validacija;dinamična validacija;sistemska biologija;Boolean network inference;gene regulatory networks;static validation;dynamic validation;systems biology;

Data

Language: English
Year of publishing:
Typology: 1.02 - Review Article
Organization: UL FRI - Faculty of Computer and Information Science
UDC: 004:575.112
COBISS: 117893635 Link will open in a new window
ISSN: 2405-8440
Views: 33
Downloads: 18
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: inferenca Boolovih mrež;gensko regulatorna omrežja;statična validacija;dinamična validacija;sistemska biologija;
Type (COBISS): Article
Pages: str. 1-21
Volume: ǂVol. ǂ
Issue: ǂno. ǂ
Chronology: 2022
DOI: 10.1016/j.heliyon.2022.e10222
ID: 16382214