AttentionDCA

Package used for the analysis of the factored self-attention mechanism through a simple one-layer DCA model at:

1. Caredda F., Pagnani A., Direct Coupling Analysis and the Attention Mechanism, biorxiv:579080

Install

This is an unregistered package: to install enter ] in the Julia repl and

pkg> add https://github.com/pagnani/AttentionDCA.jl

Use

The functions for the training are

trainer, stat_trainer, 
artrainer, stat_artrainer, 
multi_trainer, stat_multi_trainer
multi_artrainer

These take as as inputs either tuples with integer-encoded MSA and weight vector $(Z,W)$ or a path to the fasta file containing the sequences of the protein family under study. To get more details on the use of each single function use the help function in the Julia repl, e.g.

?trainer

Example

A detailed example of the use of the package can be found inside notebooks/ExampleAttentionDCA. To use it, it is necessary to clone the repository and follow the indications inside the notebook itself.

Data

All data used in this study is publicly available at GitHub/francescocaredda/DataAttentionDCA in the "data" folder.

Inquiries

Any question can be directed to francesco.caredda@polito.it