Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization

This
work presents a machine learning algorithm referred to as
the supervised inference of feature taxonomy from ensemble randomization
(SIFTER), which supports the identification of features derived from
untargeted ion mobility-mass spectrometry (IM-MS) experiments. SIFTER
utilizes random forest machine learning on three analytical measurements
derived from IM-MS (collision cross section, CCS), mass-to-charge
(m/z), and mass defect (Δm) to classify unknown features into a taxonomy of chemical
kingdom, super class, class, and subclass. Each of these classifications
is assigned a calculated probability as well as alternate classifications
with associated probabilities. After optimization, SIFTER was tested
against a set of molecules not used in the training set. The average
success rate in classifying all four taxonomy categories correctly
was found to be >99%. Analysis of molecular features detected from
a complex biological matrix and not used in the training set yielded
a lower success rate where all four categories were correctly predicted
for ∼80% of the compounds. This decline in performance is in
part due to incompleteness of the training set across all potential
taxonomic categories, but also resulting from a nearest-neighbor bias
in the random forest algorithm. Ongoing efforts are focused on improving
the class prediction accuracy of SIFTER through expansion of empirical
data sets used for training as well as improvements to the core algorithm.