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Building a Knowledge Graph with Spark and NLP for Novel Drug Recommendations
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- 1 Intro
- 2 Drug discovery is hard
- 3 AstraZeneca introduced the "5R" framework
- 4 5R has had a significant impact in improving our efficiency
- 5 We are investing in new sources of data and faster validation
- 6 We need tools to make sense of data & make better and faster decisions
- 7 Finding a drug target can be formulated as a hybrid recommendation problem • Scientists need to parse large amount of information and make a ranking prediction • Different formats, data models, locat…
- 8 Multiple objective optimization
- 9 Traditional recsys approaches
- 10 We assemble a large scale knowledge graph from public and AZ internal data
- 11 KG pipeline on
- 12 Pipeline - series of notebooks
- 13 Pipeline stages
- 14 Node dictionary
- 15 Mappings table
- 16 Edge assertions
- 17 Keep evidence & context for each assertion
- 18 Focus on NLP
- 19 Use natural language processing to extract precise information at scale
- 20 NLP Termite on Spark
- 21 Syntax parsing increases precision of entity recognition
- 22 Relationship from literatures reduce sparsity of biological KG
- 23 Language models lead to improvements in recall and precision
- 24 Learned sentence representation can be used for downstream tasks
- 25 Graph embedding pipeline
- 26 Approximate nearest neighbor search
- 27 Lessons learned
- 28 Acknowledgements
