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Found 5 papers, 4 papers with code
Pairing interacting protein sequences using masked language modeling
We introduce a method called DiffPALM that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context.
Impact of phylogeny on structural contact inference from protein sequence data
Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins.
Generative power of a protein language model trained on multiple sequence alignments
Moreover, for small protein families, our generation method based on MSA Transformer outperforms Potts models.
Protein language models trained on multiple sequence alignments learn phylogenetic relationships
Simple combinations of MSA Transformer's row attentions have led to state-of-the-art unsupervised structural contact prediction.
giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations.
