Gene regulatory network inference using PLS-based methods

• Published in: BMC bioinformatics Vol. 17; no. 1; p. 545
• Main Authors: Guo, Shun, Jiang, Qingshan, Chen, Lifei, Guo, Donghui
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.12.2016; BioMed Central
• Subjects: Analysis; Gene Expression; Gene Regulatory Networks; Genetic algorithms; Genetic regulation; Least-Squares Analysis; Methods; Models, Genetic; Oligonucleotide Array Sequence Analysis - methods; Physiological aspects; China; Partial least squares (PLS); Gene Regulatory Network inference; Gene expression data; Ensemble
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-016-1398-6

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. 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. Superior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance.