Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

Abstract

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

LGscore: A method to identify disease-related genes using biological literature and Google data

“…Their method showed high accuracy levels for atherosclerosis. Chen et al [4] presented a method that constructs a gene-regulatory network using micro-array data and literaturebased knowledge. They first extracted gene-gene relationships from the literature and then assigned random weights to the relationships.…”

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

“…In fact, some efforts in this direction have started to appear, although mostly for qualitative models [3,23,24] or with a focus on using one biological dataset combined with previous knowledge [25]. Microarrays are suitable also for quantitative integration, since they continue to provide a cost-effective platform for generating diverse quantitative data, measuring different processes and with online databases available.…”

A knowledge driven supervised learning approach to identify gene network of differentially up-regulated genes during neuronal senescence in Rattus norvegicus

“…The mathematical methods help in generating dynamic model for gene regulatory networks (Hecker et al, 2009). Recently, methods such as compressive sensing (Chang et al, 2014), evolutionary algorithms (Thomas and Jin, 2014) and literature-based knowledge (Chen et al, 2014) are being used for reconstruction of gene regulatory networks. Compressive sensing method takes advantage of the network's sparseness, it assume the biological networks are sparse networks which means most of the genes have interaction with small number of genes (Burda et al, 2011;Milo et al, 2002).…”

“…One benefit of the reconstruction methods involving evolutionary algorithms is due to the identification of both connectivity and parameters simultaneously from a given experimental data (Thomas and Jin, 2014). Chen et al (2014) reported literature based knowledge to infer gene regulatory network.…”