Paper Info
Title
Genetic Neural Networks: An artificial neural network architecture for capturing gene expression relationships.
Abstract
Motivation Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications. Results We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations. In its core, the GNN maps existing gene regulatory information in its architecture and it uses cell nodes that have been specifically designed to capture the dependencies and non-linear dynamics that exist in gene networks. These two key features make the GNN architecture capable to capture complex relationships without the need of large training datasets. As a result, GNNs were 40% more accurate on average than competing architectures (MLP, RNN, BiRNN) when compared on hundreds of curated and inferred transcription modules. Our results argue that GNNs can become the architecture of choice when building predictors of gene expression from exponentially growing corpus of genome-wide transcriptomics data. Availability and implementation https://github.com/IBPA/GNN
Year
DOI
Venue
2019
10.1093/bioinformatics/bty945
BIOINFORMATICS
Field
DocType
Volume
Data mining,Architecture,Computer science,Gene expression,Artificial intelligence,Artificial neural network
Journal
35
Issue
ISSN
Citations 
13
1367-4803
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
ameen eetemadi141.47
Ilias Tagkopoulos2709.30