Transformer-based language models are successfully used to address massive text-related
tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides …

Machine learning has been proposed as an alternative to theoretical modeling when
dealing with complex problems in biological physics. However, in this perspective, we argue …

Protein–protein interactions play crucial roles in many biological processes. Traditionally,
protein complex structures are normally built by protein–protein docking. With the rapid …

Interactions between thousands of proteins define cells' protein–protein interaction (PPI)
network. Some of these interactions lead to the formation of protein complexes. It is …

Advances in machine learning (ML) and the availability of protein sequences via high-
throughput sequencing techniques have transformed the ability to design novel diagnostic …

Abstract Machine learning has increasingly been applied to a wide range of questions in
phylogenetic inference. Supervised machine learning approaches that rely on simulated …

Computational models starting from large ensembles of evolutionarily related protein
sequences capture a representation of protein families and learn constraints associated to …

Despite their lack of a rigid structure, intrinsically disordered regions (IDRs) in proteins play
important roles in cellular functions, including mediating protein-protein interactions …

With nearly three billion players, video games are more popular than ever. Casual puzzle
games are among the most played categories. These games capitalize on the players' …

Predicting which proteins interact together from amino acid sequences is an important task.
We develop a method to pair interacting protein sequences which leverages the power of …