Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms.

Edward R Dougherty,Wenbin Liu,Liangzhong Shen,Jie Fang,Hongjia Ouyang

doi:10.1186/s13637-014-0013-2

Abstract

The inference of gene regulatory networks is a core problem in systems biology. Many inference algorithms have been proposed and all suffer from false positives. In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The performance of these algorithms is evaluated via two metrics: the normalized-edge Hamming distance and the steady-state distribution distance. Results for synthetic networks and a well-studied budding-yeast cell cycle network show that MDL-based filtering is more effective than filtering based on conditional mutual information (CMI). In addition, MDL-based filtering provides better inference than the MDL algorithm itself.Electronic supplementary materialThe online version of this article (doi:10.1186/s13637-014-0013-2) contains supplementary material, which is available to authorized users.

Highlights

A key goal in systems biology is to characterize the molecular mechanisms that govern specific cellular behavior and processes
Reducing the rate of false positives is an important issue in network inference
We address this question by using the minimum description length (MDL) principle

Summary

Introduction

A key goal in systems biology is to characterize the molecular mechanisms that govern specific cellular behavior and processes. Conditional mutual information (CMI) was proposed to tackle the false positives produced by both the chain-like and hub-based interactions [14]. Simulation results show that it is more effective than the CMI-based method and can reduce the false positives in the MDL algorithm in [5]. Because genes are removed from the network based upon their regulatory relations with other genes, false positives are troublesome They increase the amount of reduction necessary and second, they compete with true positive connections for retention in the reduced network. For the caveat, all of this is fine, so long as the accuracy of the original inference algorithm is not adversely impacted This means that, relative to some distance function between a ground-truth network and an inferred network (which quantifies inference accuracy), the distance is not increased when using the modified false-positive reducing algorithm in place of the original algorithm.

Boolean networks

Best-fit extension

Results and discussion

Conclusion