Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships
Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships
复制标题
DOI：
10.1093/bioinformatics/bty945
复制
发表时间：
2019-07-01
期刊：
影响因子：
5.8
通讯作者：
Tagkopoulos, Ilias
中科院分区：
文献类型：
--
作者：
Eetemadi, Ameen;Tagkopoulos, Ilias
动机基因表达预测是计算生物学面临的重大挑战之一。转录学数据的可获得性和人工神经网络的最新进展为建立具有深远应用的基因表达预测模型提供了前所未有的机会。结果我们提出了遗传神经网络(GNN)，这是一个在基因敲除和掌握调控因子扰动的情况下预测全基因组基因表达的人工神经网络。在其核心，GNN映射其架构中现有的基因调控信息，并使用专门设计的细胞节点来捕获基因网络中存在的依赖性和非线性动力学。这两个关键特性使GNN体系结构能够在不需要大型训练数据集的情况下捕获复杂的关系。结果，在数百个经过筛选和推断的转录模块上，GNN的准确率平均比竞争对手的架构(MLP、RNN、BiRNN)高出40%。我们的结果认为，当从指数级增长的全基因组转录数据语料库中构建基因表达预测因子时，GNN可以成为选择的架构。可用性和实现https://github.com/IBPA/GNN
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 implementationhttps://github.com/IBPA/GNN