Dr. Marie Lisandra Zepeda Menzoza - Novo Nordisk Research Centre
It is estimated that around 80% of the publicly available information is unstructured (in the form of text, videos and pictures); while the other 20% is structured (databases). Thus, in NNRCO we exploit the text data using Natural Language Processing (NLP) and text-mining approaches. It is also known that the analysis of features through machine learning models (ML) gives you a lot of information but analysing also the interactions between those features gives you even more insights. Thus, we also use network approaches, in particular, knowledge graphs (KG) to which we apply graph data science algorithms.
Our third Neo4j Health Care & Life Sciences Workshop has been set up to showcase practical solutions to common problems as well as helping to incubate collaboration, innovation and good practice. Graph databases are powerful tools that are inherently capable of managing vast quantities of data points and the web of relationships between them. As people start turning to tools like Neo4j for answers there are inevitably more questions: data modeling, performance, resilience, interoperability. These are the kinds of questions we want to help you answer.
Learn more: https://neo4j.com/use-cases/life-sciences/