Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Abstract

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.

CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types

“…An important component of our algorithm is a filter based on singular value decomposition (SVD), which amplifies biologically informative signals in the expression data (Additional files 1 and 2 ). SVD-based filters have found diverse applications in biology, such as increasing sensitivity when reverse-engineering gene regulatory networks [ 20 , 21 ] and controlling for population structure in GWAS [ 22 ], but have not been explored in the context of predicting cell type-specific expression before. In a test application to predict tissue-enriched genes (e.g.…”

Introduction to Pattern Recognition and Bioinformatics

“…A GRN inference algorithm from gene expression data based on differential equation model has been developed in [393]. Path consistency algorithm based on conditional mutual information has been employed for inferring the GRNs from gene expression data considering the nonlinear dependence and topological structure of the GRNs [390].…”

Explosion gravitation field algorithm with dust sampling for unconstrained optimization

“…The core algorithm concerned in this research is the gravitation field algorithm (GFA), which belongs to the second class (physics-based CI). GFA [22][23][24] proposed by Zheng et al in 2012 simulates the formation process of planets based on the Solar Nebular Disk Model (SNDM) [25] in astronomy. Based on the original GFA, we have developed an improved version of GFA called GFA-OD [26] (Optimal Detection).…”