Analysis, Classification and Marker Discovery of Gene Expression Data with Evolving Spiking Neural Networks

2018 ◽  
pp. 517-527
Author(s):  
Gautam Kishore Shahi ◽  
Imanol Bilbao ◽  
Elisa Capecci ◽  
Durgesh Nandini ◽  
Maria Choukri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Spiking Neural Networks ◽  
Expression Data ◽  
Marker Discovery

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

Modelling and Analysis of Temporal Gene Expression Data Using Spiking Neural Networks

2018 ◽  
pp. 571-581
Author(s):  
Durgesh Nandini ◽  
Elisa Capecci ◽  
Lucien Koefoed ◽  
Ibai Laña ◽  
Gautam Kishore Shahi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Spiking Neural Networks ◽  
Expression Data ◽  
Temporal Gene Expression

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.

Evaluating switching neural networks through artificial and real gene expression data

2009 ◽  
Vol 45 (2-3) ◽  
pp. 163-171 ◽  
Author(s):  
Marco Muselli ◽  
Massimiliano Costacurta ◽  
Francesca Ruffino
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Expression Data ◽  
Real Gene

scholarly journals Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

2020 ◽  
Author(s):  
Hryhorii Chereda ◽  
Annalen Bleckmann ◽  
Kerstin Menck ◽  
Júlia Perera-Bel ◽  
Philip Stegmaier ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Gene Expression Data ◽  
Molecular Network ◽  
Patient Specific ◽  
Expression Data ◽  
Learning Methods

AbstractMotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient.ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrphttps://frankkramer-lab.github.io/MetaRelSubNetVis/[email protected]

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