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Explainable Phenotyping

This study presents an innovative approach in healthcare that applies clustering techniques to the identification of patient phenotypes, or medically relevant groups such as diseases and conditions. While many phenotypes are known from established medical knowledge, the researchers highlight that numerous unknown phenotypes and subtypes likely remain unexplored. By clustering patient data, their work aims to reveal these hidden patterns and expand our understanding of patient health.

Decoding Proteins with Transformers

Transformer models, renowned for their ability to capture long-range dependencies in sequential data, have revolutionized protein language modeling. Pretrained models like ESM and ProtBERT, trained on massive datasets of protein sequences, leverage the Transformer architecture to learn intricate representations of protein structures and functions. These models excel at tasks such as protein function prediction, homology detection, and contact prediction, providing valuable insights into the complex world of proteins.