期刊
APPLIED SOFT COMPUTING
卷 69, 期 -, 页码 585-598出版社
ELSEVIER
DOI: 10.1016/j.asoc.2018.05.009
关键词
Gene regulatory networks; Fuzzy cognitive maps; Multi-agent genetic algorithm; Random forests
资金
- Outstanding Young Scholar Program of National Natural Science Foundation of China (NSFC) [61522311]
- General Program of NSFC [61773300]
- Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China [2017J017]
Inferring gene regulatory networks (GRNs) from expression data is an important and challenging problem in the field of computational biology. With the growth of high-throughput gene expression data, GRN inference has attracted much interest from researchers. In this paper, we focus on inferring large-scale GRNs using a fast and accurate algorithm. We first use fuzzy cognitive maps (FCMs) to model GRNs. Then, multi-agent genetic algorithm (MAGA) is used to determine regulatory links, and random forests (RF) are used as the feature selection algorithm to initialize the agents, which can reduce the search space of MAGA according to the gene ranking. We improve the genetic operators of MAGA to cope with GRN inference. The proposed algorithm is termed as MAGARF(FC)M-GRN. In the experiments, the performance of MAGARF(FCM)-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that MAGARF(FCM)-GRN can infer directed GRNs with high accuracy and efficiency. (C) 2018 Elsevier B.V. All rights reserved.
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