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Data-Driven Boolean Network Inference Using a Genetic Algorithm With Marker-Based Encoding
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DOI：10.1109/TCBB.2021.3055646.png)
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Abstract
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The inference of Boolean networks is crucial for analyzing the topology and dynamics of gene regulatory networks. Many data-driven approaches using evolutionary algorithms have been proposed based on time-series data. However, the ability to infer both network topology and dynamics is restricted by their inflexible encoding schemes. To address this problem, we propose a novel Boolean network inference algorithm for inferring both network topology and dynamics simultaneously. The main idea is that, we use a marker-based genetic algorithm to encode both regulatory nodes and logical operators in a chromosome. By using the markers and introducing more logical operators, the proposed algorithm can infer more diverse candidate Boolean functions. The proposed algorithm is applied to five networks, including two artificial Boolean networks and three real-world gene regulatory networks. Compared with other algorithms, the experimental results demonstrate that our proposed algorithm infers more accurate topology and dynamics.
Keywords:
Heuristic algorithms
Boolean functions
Biological cells
Network topology
Encoding
Inference algorithms
Topology
Boolean network inference
data driven
evolutionary algorithm
marker-based encoding
gene regulatory network
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