Neo4j Life Sciences and Healthcare Network

Daniel Himmelstein

Daniel Himmelstein’s Thesis Seminar for his PhD in Biological & Medical Informatics at UCSF.

Here are the slides and an online adaptation of the PhD Exhibit. Daniel was also interviewed on our Graphistania Podcast and created a fun Graph Gist as live documentation.

Alejandro Brenes Murillo

The proteome is the entire set of proteins that are produced or modified by an organism. It is an element that varies with time, stress, environmental conditions or distinct requirements that a cell might have. Join this talk by Alejandro Brenes Murillo to see how graph databases can be useful for proteome analysis.

At the Lamond Lab in the University of Dundee, scientists are interested in modelling and understanding protein behaviour under different conditions and dimensions of analysis.

In order to achieve this goal, they use graph databases to integrate and model the proteomics data, and study its effect on a specific proteome. The dimensions of analysis are multiple, yet be it turnover, localisation, cell cycle, protein complexes or biological response to stimuli, discovering the behaviour of proteins is key to understanding how organisms function, and how disease affects them.

Martin Preusse

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.

Simon Jupp

In this lightning talk from GraphConnect Europe 2016, Simon Jupp of the European Bioinformatics Institute discusses the application they built to track ontologies. He also discusses why they chose Neo4j over various RDF and semantic web technologies, and provides some example queries.

Irina Balaur, EISBM

An Integrative Framework for Data Management in Systems Biology and Medicine: Strategies for personalised medicine involve integration of large amounts of biomedical data, specific to multiple spatial and temporal scales, (including molecular data and patient clinical data). We have been developing a graph-database approach implemented in Neo4j to facilitate management (integration, exploration, visualisation, interpretation) of diverse types of biological and biomedical data.

Tim Williamson, Data Scientist, Monsanto

Presentation at GraphConnect SF 2015.

Thilo Muth

Today’s life science research is about genes, proteins, metabolites, relationships, interactions and biological networks. Data storing and mining brings a huge potential for biologists, however classical storage formats such as SQL and Excel involve various issues, such as scalability and performance problems with data growth, complexity and accessibility. Finally, most of the storage models are far from biological reality: Graph databases and Neo4j meet the need in life sciences for an appropriate data and database model.

The tree of life links all biodiversity through a shared evolutionary history. This project will produce the first online, comprehensive first-draft tree of all 1.8 million named species, accessible to both the public and scientific communities.

Assembly of the tree will incorporate previously-published results, with strong collaborations between computational and empirical biologists to develop, test and improve methods of data synthesis.

This initial tree of life will not be static; instead, we will develop tools for scientists to update and revise the tree as new data come in. Early release of the tree and tools will motivate data sharing and facilitate ongoing synthesis of knowledge.

Biological research of all kinds, including studies of ecological health, environmental change, and human disease, increasingly depends on knowing how species are related to each other.

Yet there is no single resource that unites knowledge of the tree of life. Instead, only small parts of the tree are individually available, generally as printed figures in journal articles.

This project will provide the global community of scientists who study the tree of life with a means to share and combine their results, and will enable large-scale studies of Earth’s biodiversity. It will also create a resource where students, educators and citizens can go to explore and learn about life’s evolutionary history.

Read more on the OpenTreeOfLife Blog