Difference between revisions of "Neural network module"
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:Generates a library of simulated DEER data for use in neural network training and validation. | :Generates a library of simulated DEER data for use in neural network training and validation. | ||
;[[deernet.m]] | ;[[deernet.m]] | ||
| − | :Uses | + | :Uses an ensemble of neural networks to extract distance distributions from DEER data. |
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;[[descramble.m]] | ;[[descramble.m]] | ||
:Weight matrix descrambling using Tikhonov smoothness criterion. | :Weight matrix descrambling using Tikhonov smoothness criterion. | ||
Revision as of 16:34, 14 June 2021
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
