STUDENTSHIP

KNETMINER AND KNOWLEDGE GRAPH ANALYTICS (APPLICATIONS NOW CLOSED)

PROJECT SUMMARY

AREA OF SCIENCE:

Bioinformatics

DURATION:

3 Months

CLOSING DATE/TIME:

Friday, April 8, 2022 - 17:00

DEPARTMENT:

Computational and Analytical Sciences

STUDENTSHIP DETAIL

KnetMiner (https://knetminer.com, see Hassani-Pak et al., 2021) is an innovative gene discovery and analytics platform using Knowledge Graphs and AI to help scientists see through the complexity of connected bioscience data. The platform searches integrated data about genomes, gene networks, phenotypes, and diseases across a range of species, ranks genes in relation to user provided keywords, and displays the evidence as interactive knowledge networks. KnetMiner, developed at Rothamsted Research in Harpenden, is an ELIXIR-UK resource and is used by large science programmes such as Designing Future Wheat, the COVID-19 International Research Team, the NASA GeneLab and European Plant Breeding Communities.

As part of the platform, we publish AI-ready data endpoints, which allow for programmatic access to our rich knowledge graphs and allow further integration with other data endpoints (eg, EBI Gene Expression Atlas), based on data representation standards. This has great potential to develop bioinformatics analyses and applications to help biologists find relevant information faster and easier. For instance, experimental data about gene expression could be combined with genetic knowledge using graph analytics, clustering algorithms or machine learning methods, to reveal new biological insights into complex traits and diseases.

We are looking for an enthusiastic student to join our R&D team and develop cutting-edge data analytics solutions using KnetMiner endpoints, which potentially would include using the Jupyter platform (including Python and packages such as Pandas or NumPy), learning graph database technologies (eg, SPARQL, Neo4j), developing data analysis and visualisation pipelines.

The project would be perfect for a bioinformatics student, or a computer science student with an interest in the life science application domain.