Filter feature selection based Boolean Modelling for Genetic Network Inference

• Published in: BioSystems 2022-11, Vol.221, p.104757-104757, Article 104757
• Main Authors: Gamage, Hasini Nakulugamuwa, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer
• Format: Article
• Language: English
• Subjects: Boolean network; Feature selection; Gene Regulatory Networks; Linear discriminant analysis; Min-redundancy max-relevance; ReliefF
• ISSN: 0303-2647; 1872-8324
• DOI: 10.1016/j.biosystems.2022.104757

The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency.