Abstract
Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network.
Highlights
One great challenge of postgenomic research is to explore complex biological pathways from genomic data such as DNA sequences, protein sequences, and gene expression profiles
This study mainly focuses on reconstructing pathway by gene expression
Pathway reconstruction methods based on gene expression have been widely discussed in the literature, there is a limitation on the gene expression methods
Summary
One great challenge of postgenomic research is to explore complex biological pathways from genomic data such as DNA sequences, protein sequences, and gene expression profiles. The network building method is widely used throughout biology to reconstruct complex biological pathways. Robert et al [1] and related studies [2,3,4,5] based on biology experiments provide the MAPK pathway (Figure 1). To reduce the cost of experiments, one possibility is to predict the activation status of these genes through their microarray expression data for inferring the pathway
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