Neural network module

DEERNet is a collection of functions that make and use deep neural networks for processing DEER data. The approach is described in detail in (https://dx.doi.org/10.1126/sciadv.aat5218). The reasons why it works so well are analysed in (https://arxiv.org/abs/1912.01498).

Using DEERNet

Import your data with elexsys2deernet.m and feed it into deernet.m - examples are provided in examples/deernet directory. You will get outputs that looks like the following:

Functions

deer_lib_gen.m

Generates a library of simulated DEER data for use in neural network training and validation.

deernet.m

Uses an ensemble of neural networks to extract distance distributions from DEER data.

descramble.m

Weight matrix descrambling using Tikhonov smoothness criterion.

dist_range.m

Distance range estimation for a given time grid.

elexsys2deernet.m

Prepares standard Bruker Elexsys datasets for input into the deernet.m function.

left_diag.m

Weight matrix descrambling using maximum diagonality criterion.

process_using.m

Runs DEER data processing using a specified neural network file.

train_one_net.m

Trains a single neural network using supplied parameters.

Service functions

quality_control.m

Internal heuristics designed to catch malformed inputs.

Version 2.6, authors: Ilya Kuprov, Steve Worswick, Jake Amey, Jake Keeley, Tajwar Choudhury