TaBooN -- Boolean Network Synthesis Based on Tabu Search
Sara Sadat Aghamiri, Franck Delaplace

TL;DR
This paper introduces TaBooN, a novel workflow that uses Tabu search to automatically synthesize Boolean networks from biological data, aiding in biological interpretation and modeling.
Contribution
The work presents an automated, data-driven method for Boolean network inference utilizing Tabu search to identify the most accurate models based on biological data.
Findings
Successfully infers Boolean networks from experimental data.
Uses Tabu search to optimize model selection.
Provides a platform for biological network analysis and prediction.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
