Graph Neural Networks (GNNs) have known an important and fast development in the last decade, with many theoretical and practical innovations. Their main feature is the capability of processing graph structured data with minimal loss of structural information. This makes GNNs the ideal family of models for processing a wide variety of biological data: metabolic networks, structural formulas of molecules, and proteins are all examples of biological data that are naturally represented as graphs. As an example, GNNs were employed, with very good results, for the prediction of protein-protein interactions. This was achieved by applying a clique detection model on graphs representing the interaction of the secondary structures of pairs of proteins. The introduction of composite GNN models, designed for processing heterogeneous graphs, has allowed researchers to study even more complex networks. For instance, drug side-effects were predicted based on a graph describing the interactions between drugs and human genes. Another very important innovation was brought by generative models, that were introduced for graph data after the success of generative models for images. In particular, GNNs were used to build a sequential model for the generation of potential drug candidates, in the form of molecular graphs, with the purpose of enhancing existing drug discovery techniques. The increasing accuracy and efficacy of these models, as well as the development of more complex biological databases, ensure even more interesting future developments in the application of GNNs to biological data.

Bongini, P., Pancino, N., Scarselli, F., Bianchini, M. (2023). BioGNN: How Graph Neural Networks Can Solve Biological Problems. In Artificial Intelligence and Machine Learning for Healthcare (pp. 211-231). Cham : Springer [10.1007/978-3-031-11154-9_11].

BioGNN: How Graph Neural Networks Can Solve Biological Problems

N. Pancino;F. Scarselli;M. Bianchini
2023

Abstract

Graph Neural Networks (GNNs) have known an important and fast development in the last decade, with many theoretical and practical innovations. Their main feature is the capability of processing graph structured data with minimal loss of structural information. This makes GNNs the ideal family of models for processing a wide variety of biological data: metabolic networks, structural formulas of molecules, and proteins are all examples of biological data that are naturally represented as graphs. As an example, GNNs were employed, with very good results, for the prediction of protein-protein interactions. This was achieved by applying a clique detection model on graphs representing the interaction of the secondary structures of pairs of proteins. The introduction of composite GNN models, designed for processing heterogeneous graphs, has allowed researchers to study even more complex networks. For instance, drug side-effects were predicted based on a graph describing the interactions between drugs and human genes. Another very important innovation was brought by generative models, that were introduced for graph data after the success of generative models for images. In particular, GNNs were used to build a sequential model for the generation of potential drug candidates, in the form of molecular graphs, with the purpose of enhancing existing drug discovery techniques. The increasing accuracy and efficacy of these models, as well as the development of more complex biological databases, ensure even more interesting future developments in the application of GNNs to biological data.
978-3-031-11153-2
978-3-031-11154-9
Bongini, P., Pancino, N., Scarselli, F., Bianchini, M. (2023). BioGNN: How Graph Neural Networks Can Solve Biological Problems. In Artificial Intelligence and Machine Learning for Healthcare (pp. 211-231). Cham : Springer [10.1007/978-3-031-11154-9_11].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1216954