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Found 11 papers, 6 papers with code
Mutant fate in spatially structured populations on graphs: connecting models to experiments
Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix.
Evolution of cooperation in deme-structured populations on graphs
Models of spatially structured populations with one individual per node of a graph have shown that cooperation, modeled via the prisoner's dilemma, can be favored by natural selection.
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.
Frequent asymmetric migrations suppress natural selection in spatially structured populations
Natural microbial populations often have complex spatial structures.
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.
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs.
Impact of population size on early adaptation in rugged fitness landscapes
Due to stochastic fluctuations arising from finite population size, known as genetic drift, the ability of a population to explore a rugged fitness landscape depends on its size.
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.
Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences
We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI.
Toward a universal model for spatially structured populations
We demonstrate that by tuning migration asymmetry, the star graph transitions from amplifying to suppressing natural selection.
