Christian Dallago

TL;DR: In this paper, the authors trained two auto-regressive language models (Transformer-XL and XLNet) on 80 billion amino acids from 200 million protein sequences (UniRef100) and one auto-encoder model on 393 billion amino acid from 2.1 billion protein sequences taken from the Big Fat Database (BFD).

TL;DR: In this paper, the authors trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids.

TL;DR: Transfer-learning succeeded to extract information from unlabeled sequence databases relevant for various protein prediction tasks and modeled the language of life, namely the principles underlying protein sequences better than any features suggested by textbooks and prediction methods.

TL;DR: Pathway Commons as mentioned in this paper is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds).

TL;DR: The EVcouplings framework is presented, a fully integrated open-source application and Python package for coevolutionary analysis that enables generation of sequence alignments, calculation and evaluation of evolutionary couplings, and de novo prediction of structure and mutation effects.