Approaches in network medicine have traditionally focused on generating insights from graphs with a single type of node and relationship. However, biology's complexity demands a richer network structure capable of integrating diverse, multi-scale information. Towards this end, we develop hetnets — networks with multiple types of nodes and relationships.
Specifically we created Hetionet — a network of biology, disease, and pharmacology. This resource encodes knowledge from millions of biomedical studies over the last half century. Version 1.0 contains 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. We host a public Neo4j database instance at https://neo4j.het.io allowing users to interact with Hetionet.
In Project Rephetio, we applied Hetionet to predict new uses for existing drugs. Our approach learned the network patterns of connectivity that differentiate treatments from non-treatments, enabling us to predict the probability of treatment for 209,168 compound–disease pairs. These predictions prioritize treatments under investigation by clinical trial.
Going forward, we're investigating more efficient algorithms for feature extraction on hetnets. In addition, we're looking to automate hetnet construction by text mining the literature. The success of hetnets will depend on the availability of openly licensed inputs. As such, I'll briefly discuss data analyses I've performed in hopes of making science more open.
Graphs are composed of:
Nodes / relationships have type:
networks with multiple node or relationship types
multilayer network, multiplex network, multivariate network, multinetwork, multirelational network, multirelational data, multilayered network, multidimensional network, multislice network, multiplex of interdependent networks, hypernetwork, overlay network, composite network, multilevel network, multiweighted graph, heterogeneous network, multitype network, interconnected networks, interdependent networks, partially interdependent networks, network of networks, coupled networks, interconnecting networks, interacting networks, heterogenous information network
Visualizing Hetionet v1.0
>>> import phd
What's the best software for storing and querying hetnets?
GitHub stats from 2016-10-09
More queries at thinklab.com/d/220
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
In pres at eLife. https://doi.org/10.1101/087619
features = metapaths
positives = treatments
Browse at het.io/repurpose/metapaths.html
Cypher query to find the top CbGbPWaD paths
Try at https://neo4j.het.io
Discuss at thinklab.com/d/224#5
Discuss at thinklab.com/d/230#14
Nice of you to share this big network with everyone; however, I think you need to take care not to get yourself into legal trouble here. …
I am not trying to cause trouble here — just the contrary. When making a meta-resource, licenses and copyright law are not something you can afford to ignore. I regularly leave out certain data sources from my resources for legal reasons.
One network to rule them all
We have completed an initial version of our network. …
Network existence (SHA256 checksum for graph.json.gz) is proven in Bitcoin block 369,898.
the hetnet search engine
powering the next generation of scholarly manuscript
The Manubot project began with the [Deep Review](https://github.com/greenelab/deep-review), where it was used to compose a highly-collaborative review article [@doi:10.1101/142760]. Other manuscripts that were created with Manubot include: + The Sci-Hub Coverage Study ([GitHub](https://github.com/greenelab/scihub-manuscript), [HTML manuscript](https://greenelab.github.io/scihub-manuscript/)) [@doi:10.7287/peerj.preprints.3100] + Michael Zietz's Report for the Vagelos Scholars Program ([GitHub](https://github.com/zietzm/Vagelos2017), [HTML manuscript](https://zietzm.github.io/Vagelos2017/)) [@doi:10.6084/m9.figshare.5346577]
1. Opportunities And Obstacles For Deep Learning In Biology And Medicine
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Wei Xie, Gail L. Rosen, … Casey S. Greene
Cold Spring Harbor Laboratory (2017-05-28) https://doi.org/10.1101/142760
2. Sci-Hub provides access to nearly all scholarly literature
Daniel S Himmelstein, Ariel R Romero, Stephen R McLaughlin, Bastian Greshake Tzovaras, Casey S Greene
PeerJ Preprints (2017-07-20) https://doi.org/10.7287/peerj.preprints.3100
3. Vagelos Report Summer 2017
Figshare (2017) https://doi.org/10.6084/m9.figshare.5346577
Automatically converted to rich text
Automatic bibliographic metadata
1. Modify the manuscript source
2. Continuous integration rebuilds the manuscript
Timestamped on the Bitcoin blockchain via OpenTimestamps
3. Continuous deployment back to GitHub
Pull requests for manuscript collaboration
Get started at tiny.cc/manubot