Natural language processing (NLP) is a field of computer science concerned with automated
text and language analysis. In recent years, following a series of breakthroughs in deep and …

Recently, deep learning models, initially developed in the field of natural language
processing (NLP), were applied successfully to analyze protein sequences. A major …

Deep neural-network-based language models (LMs) are increasingly applied to large-scale
protein sequence data to predict protein function. However, being largely black-box models …

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

The prediction of protein structures from sequences is an important task for function
prediction, drug design, and related biological processes understanding. Recent advances …

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

Advanced Artificial Intelligence (AI) enabled large language models (LLMs) to revolutionize
Natural Language Processing (NLP). Their adaptation to protein sequences spawned the …

Language models have recently emerged as a powerful machine-learning approach for
distilling information from massive protein sequence databases. From readily available …

Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

Protein representations from deep language models have yielded state-of-the-art
performance across many tasks in computational protein engineering. In recent years …