Name
Abstract | ||
|---|---|---|
Inference of gene regulatory networks (GRNs) based on experimental data is a challenging task in bioinformatics. In this paper, we present a bi-objective minimization model (BoMM) for inference of GRNs, where one objective is the fitting error of derivatives, and the other is the number of connections in the network. To solve the BoMM efficiently, we propose a multi-objective evolutionary algorithm (MOEA), and utilize the separable parameter estimation method (SPEM) decoupling the ordinary differential equation (ODE) system. Then, the Akaike Information Criterion (AIC) is employed to select one inference result from the obtained Pareto set. Taking the S-system as the investigated GRN model, our method can properly identify the topologies and parameter values of benchmark systems. There is no need to preset problem-dependent parameter values to obtain appropriate results, and thus, our method could be applicable to inference of various GRNs models. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Evolutionary algorithm,Artificial intelligence,Estimation theory,Ode,Mathematical optimization,Akaike information criterion,Inference,Algorithm,Network topology,Gene regulatory network,Mathematics,Machine learning,Pareto principle |
DocType | Volume | Citations |
Journal | abs/1512.05055 | 0 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yu chen | 1 | 25 | 7.06 |
| Xiufen Zou | 2 | 272 | 25.44 |