Abstract
Inferring Boolean networks is crucial for modeling and analyzing gene regulatory networks from a systematic perspective. However, the state-of-the-art algorithms cannot accurately infer the topology and dynamics of Boolean networks due to the lack of an efficient approach to representing the unknown Boolean functions and the over-fit problem caused by the noise in time-series data. To address these problems, we propose a novel inference algorithm using a genetic algorithm with novel polynomial representation and encoding scheme (GAPORE) to reconstruct large-scale Boolean networks accurately. First of all, a novel symbolic polynomial representation method is introduced to efficiently represent the unknown Boolean functions of the candidate Boolean network as the symbolic polynomial dynamical equations. Then, a novel encoding scheme is developed to flexibly encode the symbolic polynomial dynamical equations by varying the effective lengths of the chromosomes. To reduce the over-fit problem, the -norm regularization is designed into the fitness evaluation in view of the network sparsity. In addition, the local search strategy is embedded into the hybrid genetic algorithm framework to strengthen the search capability. Extensive experiments demonstrate that GAPORE can infer the large-scale Boolean networks more accurately than state-of-the-art algorithms from the noisy time-series data.
Original language | English |
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Article number | 107277 |
Number of pages | 13 |
Journal | Knowledge-Based Systems |
Volume | 228 |
Early online date | 3 Jul 2021 |
DOIs | |
Publication status | Published - 27 Sept 2021 |
Bibliographical note
Funding Information:This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1701903 and in part by the National Natural Science Foundation of China under Grant 61973138. Shan He would like to thank the Southern University of Science and Technology (SUSTech) for the sabbatical visiting program.
Publisher Copyright:
© 2021 Elsevier B.V.
Keywords
- Data-driven
- Boolean network
- Polynomial Boolean representation
- Hybrid genetic algorithm
- Dominant bit encoding scheme
ASJC Scopus subject areas
- Software
- Management Information Systems
- Information Systems and Management
- Artificial Intelligence