Psychometrics Data Science

Background Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins. Results Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best. Conclusion We conclude that both predicate and provenance information provide added value for drug efficacy screening.

By Rob Hill, CRO at ProFinda Can a Machine Learning powered Knowledge Graph transform Talent Acquisition? The true transformation of talent acquisition will only occur when we start to focus the power of Machine Intelligence on understanding and mapping out the knowledge contained within an organisation. This can be achieved by building a knowledge graph of … The power of Machine Learning to drive Talent Acquisition Read More »

At Beamery, we are pushing the boundaries of how modern recruitment is being done.

A few years after Google announced that their knowledge graph allowed searching for things, not strings, knowledge graphs...

His team is building a talent mapping database using Neo4j technology to build a knowledge graph to show the relationships between people, skills, and projects. VentureBeat: How are you using those categories to build a data model? Those elements consist of knowledge, skills, abilities, tasks, workforce characteristics, licensing, and education. And it’s similar for programs: we can connect back to what knowledge, skills, and tasks a person needs for each project. Within a graph database or knowledge graph, you can easily add information as you get it without messing up your schema or your data model.

Check out how David Meza from NASA combined knowledge graph and graph algorithms to find hidden skills within the organization.

David Meza, senior data scientist at NASA, and his team are building a talent mapping database to better identify talent for jobs.