Breakthrough methods in machine learning (ML), protein structure prediction, and novel
ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models …

Although remarkable advances have been reported in high-throughput sequencing, the
ability to aptly analyze a substantial amount of rapidly generated biological …

The prediction of molecular phenotypes from DNA sequences remains a longstanding
challenge in genomics, often driven by limited annotated data and the inability to transfer …

Adapting language models to protein sequences spawned the development of powerful
protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein …

Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We
demonstrate that large-scale pre-trained transformers can significantly improve the state-of …

Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …

Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …

Abstract Since 1992 PredictProtein (https://predictprotein. org) is a one-stop online resource
for protein sequence analysis with its main site hosted at the Luxembourg Centre for …

Deep generative models are increasingly powerful tools for the in silico design of novel
proteins. Recently, a family of generative models called diffusion models has demonstrated …

Abstract Rhea (https://www. rhea-db. org) is an expert-curated knowledgebase of
biochemical reactions based on the chemical ontology ChEBI (Chemical Entities of …