糖心TV

Spanning cycles in random directed graphs.

Robert Hoehndorf, King Abdullah University, Saudi Arabia

Learning from semantic biological data

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, and how
these methods can help to improve the prediction of protein
functions.