Big Data in Genomics: How Neo4j helps to develop new drugs
Biomedical research generates vast amounts of data. New experimental technologies like DNA sequencing, metabolomics and proteomics drive the fast growth of available information and lead to a better understanding of the molecular organization of life.
But with big data comes a big question: How do we transform unstructured data into actionable knowledge? In the case of biomedical research, the key problem is to integrate the large pile of highly heterogenous data and use it for personalized therapies and drug development. Graph databases are an ideal way to represent biomedical knowledge and offer the necessary flexibility to keep up with scientific progress. A well-designed data model and Cypher queries can deliver in seconds what previously took days of manual analysis.
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- CloudNative London 2019 (in London on 25th - 27th September 2019)
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Thanks to our sponsors
Big Data in Genomics: How Neo4j helps to develop new drugs
Martin Preusse
Martin Preusse studied biochemistry at TU München and is currently finishing his PhD in bioinformatics at the Institute for Computational Biology (Helmholtz Zentrum München, Germany). He focuses on integration of heterogeneous experimental data sets to understand cellular decision-making. Next to academic research, he is working on a startup that develops data solutions for biomedical research and biotech industry.
Biomedical research generates vast amounts of data. New experimental technologies like DNA sequencing, metabolomics and proteomics drive the fast growth of available information and lead to a better understanding of the molecular organization of life.
But with big data comes a big question: How do we transform unstructured data into actionable knowledge? In the case of biomedical research, the key problem is to integrate the large pile of highly heterogenous data and use it for personalized therapies and drug development. Graph databases are an ideal way to represent biomedical knowledge and offer the necessary flexibility to keep up with scientific progress. A well-designed data model and Cypher queries can deliver in seconds what previously took days of manual analysis.
YOU MAY ALSO LIKE:
- Elasticsearch for Developers with Itamar Syn-Hershko (in London on 23rd - 25th September 2019)
- Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
- CloudNative London 2019 (in London on 25th - 27th September 2019)
- Haskell eXchange 2019 (in London on 10th - 11th October 2019)
- IBM AI London July (in London on 16th July 2019)
- Graphs for recommendation engines & taxonomies in the semantic landscape (in London on 16th July 2019)
- Keynote: AI, Natural Stupidity and Swimming Submarines (SkillsCast recorded in July 2019)
- The Subtle Art of Recommendation (Algorithms) (SkillsCast recorded in July 2019)
Thanks to our sponsors
Big Data in Genomics: How Neo4j helps to develop new drugs
Martin Preusse
Martin Preusse studied biochemistry at TU München and is currently finishing his PhD in bioinformatics at the Institute for Computational Biology (Helmholtz Zentrum München, Germany). He focuses on integration of heterogeneous experimental data sets to understand cellular decision-making. Next to academic research, he is working on a startup that develops data solutions for biomedical research and biotech industry.