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Symbolic AI in Computational Biology; applications to disease gene and drug target identification

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The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge graph embeddings for analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets. I will also show how information from text-mining can be combined in a multi-modal machine learning model to further improve predictive performance of these models, and how these methods can help to improve interpretation of causative genomic variants in personal genomic sequence data.

This talk is part of the Computational and Systems Biology series.

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