Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

TL;DR

This paper compares the stability and performance of a novel neural network-based gene network inference method, RegnANN, with existing algorithms, revealing that all methods exhibit instability but RegnANN achieves notably high MCC scores.

Contribution

The paper introduces RegnANN, a multilayer perceptron-based method for gene network inference, and provides a systematic stability and performance comparison with established algorithms.

Findings

All algorithms show instability in network topology reconstruction.

RegnANN achieves MCC scores comparable or superior to existing methods.

Network inference methods are generally affected by data and structural variability.

Abstract

Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat)