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      Deep learning: new computational modelling techniques for genomics

      , , ,  
      Nature Reviews Genetics
      Springer Nature

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          The graph neural network model.

          Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.
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            Taking the Human Out of the Loop: A Review of Bayesian Optimization

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              Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

              T. Golub (1999)
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                Author and article information

                Journal
                Nature Reviews Genetics
                Nat Rev Genet
                Springer Nature
                1471-0056
                1471-0064
                April 10 2019
                Article
                10.1038/s41576-019-0122-6
                30971806
                52de6409-1aa9-4f39-87b9-fc4df7f05029
                © 2019

                http://www.springer.com/tdm

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