Ensemble of Elman neural networks and support vector machines for reverse engineering of gene regulatory networks

Authors:
S. I. Ao;V. Palade
Affiliations:
Oxford University Computing Laboratory, University of Oxford, Wolfson Building, Parks Road, Oxford, UK;Oxford University Computing Laboratory, University of Oxford, Wolfson Building, Parks Road, Oxford, UK
Venue:
Applied Soft Computing
Year:
2011

Citing 18
Cited 1

A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Making large-scale support vector machine learning practical

Advances in kernel methods
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference of a gene regulatory network by means of interactive evolutionary computing

Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Predicting Time Series with Support Vector Machines

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
On Learning Gene Regulatory Networks Under the Boolean Network Model

Machine Learning
An ensemble of neural networks for weather forecasting

Neural Computing and Applications
Discovering Gene Networks with a Neural-Genetic Hybrid

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data

Bioinformatics
Missing value estimation for DNA microarray gene expression data: local least squares imputation

Bioinformatics
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review

Neural Computation
Causality and pathway search in microarray time series experiment

Bioinformatics
Weather analysis using ensemble of connectionist learning paradigms

Applied Soft Computing
Boolean dynamics of genetic regulatory networks inferred from microarray time series data

Bioinformatics
Soft computing methods to predict gene regulatory networks: An integrative approach on time-series gene expression data

Applied Soft Computing
Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach

Computational Biology and Chemistry
The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks

IEEE Transactions on Neural Networks

Reverse engineering of gene regulatory networks using flexible neural tree models

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, recurrent Elman neural networks (ENNs) and support vector machines (SVMs) have been used for temporal modeling of microarray continuous time series data. An ensemble of ENN and SVM models is proposed to further improve the prediction accuracy of the individual models. The prediction results on the simulated non-stationary datasets and the real biological datasets outperform the results of the other existing approaches. In order to provide the neural networks with explanation capabilities, a pedagogical rule extraction technique has been proposed to infer the output of our proposed ensemble system. The proposed pedagogical rule extraction technique is a two-step test of causality and Pearson correlation for the network inference between the causal gene expression inputs and their predicted outputs. The results of the network inference demonstrate that the gene regulatory network can be reconstructed satisfactorily with the proposed approach.