Post #784

Graph ML News (July 22nd) - ICML’23, AI for Science survey ICML time! Michael will be representing the Graph ML channel in the infamous, 3-of-a-kind, limited edition t-shirt, drop him a line if you’d like to chat. Big labs started to announce their presence and accepted papers (not just graph papers though), eg, Google DeepMind, Meta AI, Amazon, Microsoft, Apple. If you didn’t make it to ICML this year, consider a fresh selection of the weekend reading: 📚Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems by Xuan Zhang and 60+ famous authors is a massive 260-page survey on geometric models in scientific applications spanning molecules, proteins, quantum mechanics, PDEs, and materials discovery. Contextualizing Protein Representations Using Deep Learning on Protein Networks and Single-Cell Data by Michelle M Li et al from Marinka Zitnik’s lab at Harvard. Quote: “We introduce PINNACLE, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-PINNACLE protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, PINNACLE provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs.”