A Boolean network inference from time-series gene expression data using a statistical method

Barman, Shohag; Gope, Hira Lal; Kwon, Yung-Keun

doi:10.1145/3377049.3377080

Cited by 1 publication

(1 citation statement)

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“…The introduced method exploited an existing method, MIBNI, in the first stage to find an optimal solution and then GABNI if the first method fails due to the degree of complexity of an underlying regulatory function. Also in [6] Barman and his colleagues propose a pipeline for the gene regulatory network inference from time-series gene expression data by applying a statistical method called the chi-square method to infer a Boolean network from time-series gene expression data. They also suggested that structural accuracy can be increased by combining the chi-square test with neural networks as the perspective of their methodology.…”

Section: Introductionmentioning

confidence: 99%

TaBooN -- Boolean Network Synthesis Based on Tabu Search

Aghamiri¹,

Delaplace²

2020

Preprint

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Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue.In this study, we present taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of Boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the taboon work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. taboon is an automated method for Boolean Network inference from experimental data that can also assist to evaluate and optimize the dynamic behaviour of the biological networks providing a reliable platform for further modelling and predictions.

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Section: Introductionmentioning

confidence: 99%