Author name | Symeon Panagiotoglou |
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Title | Representational learning on biological data. A study on polypharmacy side-effects and graph embeddings |
Year | 2021-2022 |
Supervisor | Anastasia Krithara AnastasiaKrithara |
In recent years graphs, graph neural networks and graph embedding techniques are getting more attention in the area of machine learning in general, with biological applications being a major drive. Using Decagon, a graph neural network that predicts polypharmacy side-effects, as our starting point, we ιmplement a number of baseline models in order to identify the aspects that play the bigger part in predicting side-effects among pairs of drugs. Later, we focus on a subset of the initial dataset containing only the rarest side-effects and experiment with well known models from the graph embeddings area. We examine whether a normalization of the feature vectors in a tf-idf fashion helps a message passing network improve its performance. Finally, we use AnyBURL, a rule based model, to identify patterns in our data.