期刊
BMC BIOINFORMATICS
卷 17, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12859-016-1398-6
关键词
Gene Regulatory Network inference; Gene expression data; Partial least squares (PLS); Ensemble
类别
资金
- Research Fund for National Natural Science Foundation of China (General Program) [61274133]
- Shenzhen Technology Development Foundation Grant [CXZZ20150813155917544]
Background: Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. Results: We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm. Then, a statistical technique is used to refine the predictions in our method. The proposed method was evaluated on the DREAM4 and DREAM5 benchmark datasets and achieved higher accuracy than the winners of those competitions and other state-of-the-art GRN inference methods. Conclusions: Superior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance.
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