“Science has explored the microcosmos and the macrocosmos; we have a good sense of the lay of the land. The great unexplored frontier is complexity.” – Heinz Pagels
Anyone working in the life sciences is well aware of the scale and complexity of the data generated by their work. This has long been the case and a well covered topic in both of our previous workshops. In an increasingly digitised world, finding meaning in ever growing, disparate datasets is fast moving beyond the capacity of a single human mind. The challenge is exacerbated by an explosion of openly available information like scientific publications, annotations, ontologies and APIs that may have a direct bearing on particular areas of interest.
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.
View the decks from the presentations of the workshop.
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04:10:03
Neo4j Health Care & Life Sciences Workshop Day 1
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03:53:19
Neo4j Health Care & Life Sciences Workshop Day 2
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26:01
Neo4j Health Care & Life Sciences Workshop Day 1 – Opening
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24:05
Graph data science and knowledge graphs for target discovery for cardiometabolic diseases
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20:56
Mapping the OMOP Common Data Model to Neo4j for Pneumonia Therapy Response: SCRIPT Case Study
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25:24
Building complex Web Applications for Health Care using Neo4j
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29:03
Pegasus: a knowledge graph to support drug discovery
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14:34
HealthECCO: Bringing the world’s health knowledge to research and medical decision makers
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15:55
Analyzing Perturbed Co-Expression Networks in Cancer Using a Graph Database
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26:57
Lifelike – a graph-powered knowledge reconstruction and mining platform to accelerate research
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19:50
Graph-centric framework for translational clinical proteomics
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18:08
From Queries to Algorithms to Advanced ML: 3 Pharmaceutical Graph Use Cases
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22:43
Neo4j for Bacterial Genomes
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25:51
Modelling Multi Omics data in Neo4j to identify targets for strain development
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22:38
Unveiling the knowledge in knowledge graphs
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16:03
Evaluating Drug Safety Data Using Graph Databases
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38:23
Clinical Knowledge Graph with Spark NLP and Neo4j
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18:05
Neo4j Health Care & Life Sciences Workshop Closing Panel Discussion
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