🙌We are beyond excited to share our Perspective on graph representation learning for biomedicine and healthcare!🥳 @natBME @KexinHuang5 @marinkazitnik @HarvardDBMI
nature.com/articles/s4155… (1/4)
nature.com/articles/s4155… (1/4)
Graphs are pervasive in #biomedicine & #healthcare, from the molecular structure of proteins to population-scale healthcare systems!
We first describe the key algorithmic paradigms of graph representation learning & their successes and limitations in this space.
(2/4)
We first describe the key algorithmic paradigms of graph representation learning & their successes and limitations in this space.
(2/4)
We then carefully survey and highlight notable applications of graph representation learning in biomedicine and healthcare!
#molecules #diseases #therapeutics #histopathology #singlecell #precisionmedicine (3/4)
#molecules #diseases #therapeutics #histopathology #singlecell #precisionmedicine (3/4)
Finally, we showcase 4 areas in which graph ML has had (+ will continue to make) great impact: Gene embeddings w/single-cell resolution, granular disease embeddings, cell-line specific drug-interaction predictions, and personalized patient diagnosis & treatment predictions! (4/4)
Full-text access link here! rdcu.be/cYE8G
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