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Dr. Davide Mottin, PhD
Postdoc @ Knowledge Discovery and Data Mining Group
Hasso-Plattner-Institut an der Universität Potsdam
Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam
Hasso-Plattner-Institut für Softwaresystemtechnik GmbH, Potsdam
Amtsgericht Potsdam, HRB 12184, Geschäftsführung: Prof. Dr. Christoph Meinel
Greetings List,Thanks Michael. Since I'm new to this mailing list, I thought I'd introduce myself. I'm a data scientist at UPenn in the Greene Lab. I started using Neo4j when working on Project Rephetio to predict new uses for exiting drugs. For this project, we created an integrative network of biomedical knowledge called Hetionet. We host the neo4j instance publicly at https://neo4j.het.io.I saw Michael's earlier email about the proceedings from the Life Sciences Meetup in Berlin. Was really excited to see so many applications of Neo4j for biomedical hetnets.Going forward, I'm hoping to transition a bit from constructing hetnets to algorithm development. As many here know, most traditional graph algorithms are oblivious to node/relationship types, rendering them useless for hetnets. One current project we have along these lines is called hetmech where we aim to be able to translate between nodes of different types. For example, a user could provide a set of disease symptoms and we would translate those to biological pathways.Anyways I'm sure many of us will cross paths in the future if we haven't already. Glad to be on this list!Best,Daniel
On Sat, Jun 24, 2017 at 5:06 PM 'Michael Hunger' via neo4j-biotech <neo4j-biotech@googlegroups.com> wrote:
--Includes full transcript, slides and video recording of the talk.Have a look, really impressive work.My personal favorite is how Daniel generated Neo4j-Browser guides for each of the proteins in the database on hetionet: http://het.io/Cheers, MichaelSummary
Himmelstein started his PhD research with the question: How do you teach a computer biology? He found the answer in a heterogenous network (a.k.a., “HetNet”), which turned out to be another term for a labelled property graph.
After an attempt to create his own Python package for querying HetNets, Himmelstein turned to Neo4j. By importing open source drug and genetic information, he has developed a graph with more than 2 million relationships that can be mined for drug repurposing – in other words, finding new treatment uses for drugs that are already on the market – via a growing dataset of matching compound-disease pairs.
For each of the current 200,000 compound-disease pairs, his project computes the prevalence of many different types of paths and then uses a machine learning classifier to identify the patterns of the network, or the paths, that are predictive of treatment or efficacy. As an example, Himmelstein shows you how his HetNet project helped identify bupropion as a drug that not only treats depression but also nicotine dependence.
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