Inferring Gene Regulatory Networks Based on a Hybrid Parallel Genetic Algorithm and the Threshold Restriction Method.
Inferring gene regulatory networks (GRNs) is a challenging computational task in system biology. Many inference algorithms have been proposed along with related modifications to various problems. Every algorithm has its own advantages and drawbacks. In particular, the efficiency of each algorithm is not as good as people expect. A novel inference algorithm is proposed in this paper that can be divided into two parts. In the first part, the pre-computational part, two tasks must be accomplished: singular value decomposition for solution space determination and the threshold restriction method for redundant edge deletion. The second part of the algorithm is a hybrid parallel genetic algorithm. In this part, a parallel genetic algorithm is used for a first quick search, after which hill climbing is used for an exact search. The proposed algorithm is validated on both melanoma and type II diabetes GRNs and is compared with other algorithms. The efficiency of our algorithm was tested with different numbers of echoes and nodes. The cross-validation results confirmed the effectiveness of our algorithm, which significantly outperforms other algorithms.
['Zheng|Ming|M|', 'Zhang|Shugong|S|', 'Zhou|You|Y|', 'Liu|Guixia|G|']
D000465:Algorithms D003924:Diabetes Mellitus, Type 2 D053263:Gene Regulatory Networks D052138:Genes, Neoplasm D006801:Humans D008545:Melanoma
Gene regulatory network Threshold restriction method Gene expression profile Oncogene