Neural network module

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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

Using DEERNet

Simply feed your DEER data to deernet.m or download DeerAnalysis2018, which is easy to use and has DEERNet integrated.

Training your own networks

  1. Use deer_lib_gen.m to build a training database.
  2. Use train_one_net.m repeatedly to create a netset.
  3. Use netset_curate.m to eliminate low-performance networks.
  4. Use the resulting netset with deernet.m or deernet_bckg.m

Functions

deer_lib_gen.m
Generates a library of simulated DEER data for use in neural network training and validation.
deer_resample.m
Resamples a DEER trace to fit the number of digitsation points expected by the neural network.
deernet.m
Uses a curated ensemble of neural networks to extract the distance distribution from DEER data.
deernet_bckg.m
Uses a curated ensemble of neural networks to extract the background signal from DEER data.
elexsys2deernet.m
Prepares standard Bruker Elexsys datasets for input into the deernet.m function.
netset_curate.m
Evaluates an ensemble of neural networks and decides which ones are best.
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.


Version 2.2, authors: Ilya Kuprov, Steve Worswick