Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks

2019 ◽  
Vol 11 (4) ◽  
pp. 599-613 ◽  
Author(s):  
Elisa Capecci ◽  
Jesus L. Lobo ◽  
Ibai Laña ◽  
Josafath I. Espinosa-Ramos ◽  
Nikola Kasabov
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Time Series ◽  
Gene Expression Data ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Spiking Neural Networks ◽  
Expression Data ◽  
Gene Interaction Networks ◽  
Time Series Gene Expression

Identifying functional modules using energy minimization with graph cuts

2021 ◽  
Vol 16 ◽  
Author(s):  
Yuanyuan Chen ◽  
Xiaodan Fan ◽  
Cong Pian
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Energy Minimization ◽  
Expression Profiles ◽  
Gene Interaction ◽  
Graph Cuts ◽  
Interaction Networks ◽  
Functional Modules ◽  
Expression Data ◽  
Gene Interaction Networks

Aims: The aim of this article was to find functional (or disease-relevant) modules using gene expression data. Background: Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles. Objective: We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules. Method: We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle. Result: Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy. Conclusion: We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks.

scholarly journals A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramin Hasibi ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Biological Networks ◽  
Molecular Interaction ◽  
Interaction Networks ◽  
Omics Data ◽  
Expression Data ◽  
Molecular Interaction Networks ◽  
Graph Neural Networks

Abstract Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

scholarly journals Clustering short time series gene expression data

2005 ◽  
Vol 21 (Suppl 1) ◽  
pp. i159-i168 ◽  
Author(s):  
J. Ernst ◽  
G. J. Nau ◽  
Z. Bar-Joseph
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Expression Data ◽  
Short Time Series ◽  
Time Series Gene Expression ◽  
Short Time
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