Here's our pre-print describing our GPT-3 based knowledge extraction tool SPIRES: https://t.co/8b57yidudx. Great work from @harry_caufield et al! SPIRES allows you to specify a knowledge schema (in @linkml_data) and then populate instances of that schema from unstructured text
A pleasure to collaborate with Elena Casiraghi on so many cool projects! Here's the latest:
A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative
https://t.co/O4w5Q75oZB
If you work in a Clinical Genetics department that recruited to the 100k Genome Project I *highly* recommend encouraging one of your clinical genetics trainees to review the top 5 exomiser results in your undiagnosed patients (particularly the trios).
This crucial research will help fill the gaps in our understanding of how #LongCOVID works and develop more effective treatments for those still suffering. https://t.co/bhYvMNEvav
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
Scientific #publications are a treasure trove, but integrating their knowledge into #graph#machinelearning is more challenging than it needs to be! 🧵 1/4
Pushing the ✉️ of 🍇's @psresnik 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.
Animations >> Words!
Here's @justaddcoffee's KGCOVID19 embedding (~574k nodes, ~18M edges), animated in ~4m on my desktop using 🍇's @rustlang implementation (with #Python bindings) of @tangjianpku's First-order LINE.
Use it with @googleslides!
💻: https://t.co/SLgBmuhlqg
The new version of 🍇, 0.1.10, is out! We introduce support for 14 MMAP-ed graph embedding algorithms, plus a significantly extended set of tutorials!
E.g. TSNEs of 36 node embeddings of Cora, find the code here: https://t.co/iX6rmHXKcJ
Our latest paper identifying subtypes of #longcovid using machine learning on EHR data from the National COVID Cohort Collaborative (N3C):
https://t.co/jWw7OkSDTS
After a lot of hard work by many people it’s finally done: Simple Standard for Sharing Ontological Mappings (SSSOM). Thank you to all contributors! https://t.co/9QGvrFd7ZL