Prototypical Clustering Networks for Dermatological Disease Diagnosis

Paper will be presented at the ML4D workshop at #NIPS2018

Link: https://arxiv.org/abs/1811.03066

#nn #bio #medical

​​(Re)Discovering Protein Structure and Function Through Language Modeling

Trained solely on unsupervised language modeling, the Transformer's attention mechanism recovers high-level structural (folding) and functional properties of proteins!

Why this is important: traditional protein modelling requires lots of computational power. This might be a key to more efficient structure modelling. Protein structure => function. Function => faster drug research and understanding of diseases mechanisms.

Blog: https://blog.einstein.ai/provis/
Paper: https://arxiv.org/abs/2006.15222
Code: https://github.com/salesforce/provis

#DL #NLU #proteinmodelling #bio #biolearning #insilico

​​DeepMind significally (+100%) improved protein folding modelling

Why is this important: protein folding = protein structure = protein function = how protein works in the living speciment and what it does.
What this means: better vaccines, better meds, more curable diseases and more calamities easen by the medications or better understanding.

Dataset: ~170000 available protein structures from PDB
Hardware: 128 TPUv3 cores (roughly  equivalent to ~100-200 GPUs)

Link: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology

#DL #NLU #proteinmodelling #bio #biolearning #insilico #deepmind #AlphaFold

Protein Folding explained

#deepmind didn’t stop at delivering the improved technology for folding #proteinfolding . They also provided really cool video explaining why is that so cool.

YouTube: https://www.youtube.com/watch?v=KpedmJdrTpY&feature=emb_title

#explained #bio #proteinmodelling