GRAPE (@grapelib) 's Twitter Profile
GRAPE

@grapelib

🍇 GRAPE is a Rust/Python library for high-performance Graph Representation learning, Predictions and Evaluations.

ID: 1537071035639599105

linkhttps://github.com/AnacletoLAB/grape calendar_today15-06-2022 13:54:47

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From 🍇 V 0.1.19, we integrated Bioregistry ❤️ in the graph retrieval, which includes all of the OBO Foundry #KnowledgeGraph made available by J. Harry Caufield's KGOBO! 🚄 Just enable the flag, and you are set to go!

From 🍇 V 0.1.19, we integrated <a href="/bioregistry/">Bioregistry</a> ❤️ in the graph retrieval, which includes all of the <a href="/OBOFoundry/">OBO Foundry</a> #KnowledgeGraph made available by <a href="/harry_caufield/">J. Harry Caufield</a>'s KGOBO!

🚄 Just enable the flag, and you are set to go!
GRAPE (@grapelib) 's Twitter Profile Photo

Introducing #slurm across 🍇 #GraphML evaluation pipelines to make 🧪 happen faster! We thank Peter Robinson & Lauren Rekerle for testing it on The Jackson Laboratory's #HPC! ❤️ 💻:github.com/AnacletoLAB/gr…

Introducing #slurm across 🍇 #GraphML evaluation pipelines to make 🧪 happen faster!

We thank <a href="/pnrobins/">Peter Robinson</a> &amp; <a href="/lnrekerle/">Lauren Rekerle</a> for testing it on <a href="/jacksonlab/">The Jackson Laboratory</a>'s #HPC! ❤️

💻:github.com/AnacletoLAB/gr…
GRAPE (@grapelib) 's Twitter Profile Photo

🎥>>🗣️ #8: Mikaela Koutrouli's FAVA functional association networks, embedded using Bryan Perozzi's DeepWalk + SkipGram with Right Laplacian sampling by Luca Cappelletti & Tommaso Fontana Done in ~2m on my desktop! ⚡ The edge prediction looks excellent (holdout 70/30)! ❤️

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Pushing the ✉️ of 🍇's Philip Resnik score implementation by computing 3T, i.e. 3*10^12, scores from NCBI Taxonomy (2438821 nodes) upper triangular matrix. This is heavily parallelized and takes ≈3h on a 💻 with 8GBs of RAM and 96 cores.

Pushing the ✉️ of 🍇's <a href="/psresnik/">Philip Resnik</a> score implementation by computing 3T, i.e. 3*10^12, scores from <a href="/NCBI/">NCBI</a> Taxonomy (2438821 nodes) upper triangular matrix.

This is heavily parallelized and takes ≈3h on a 💻 with 8GBs of RAM and 96 cores.
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Woohoo! #GRAPE just hit 100 stars on GitHub! Thank you to all the amazing developers who have supported our graph representation learning library. We couldn't have done it without you! 🍇💜🍇 #opensource #machinelearning

Woohoo! #GRAPE just hit 100 stars on GitHub! Thank you to all the amazing developers who have supported our graph representation learning library. We couldn't have done it without you! 🍇💜🍇 #opensource #machinelearning