Reposted by Michael Marty
(JPR) Evaluating
a Lightweight Neural Network for Aggregate
Isotope Distribution Prediction: AbstractIsotope distribution prediction is an important part
of mass spectrometry
data analysis. A variety of strategies have been developed, including
brute-force polynomial methods and… #MassSpecRSS
dlvr.it
Evaluating
a Lightweight Neural Network for Aggregate
Isotope Distribution Prediction
AbstractIsotope distribution prediction is an important part
of mass spectrometry
data analysis. A variety of strategies have been developed, including
brute-force polynomial methods and Fourier-transform (FT) convolution
methods. Here, we present a novel neural network (NN) approach to
isotope prediction. These NN-based tools are distributed in a new
package, IsoGen, alongside FT-based tools. We show that NNs perform
as well as existing approaches when predicting isotope distributions
for molecules with natural isotope ratios. We then demonstrate the
capability of NNs for transfer learning, a method by which existing
models that have been trained to perform one task can be reused as
a starting point to train models for a second task. Here, an NN that
has been trained to predict isotope distributions for molecules with
natural isotope ratios can be retrained to predict distributions for
molecules with perturbed isotope ratios. We also demonstrate that
these training distributions do not need to be produced theoretically
but can be extracted from experimental data in which the underlying
isotopic composition is not known. Overall, NNs provide a robust method
for isotope prediction that can be extended to applications in which
the isotope distributions can be measured but not easily predefined.
