Title: Simulation assisted machine learning,
Abstract: Predicting how a proposed cancer treatment will affect a given tumor can be
cast as a machine learning problem, but the complexity of biological systems,
the number of potentially relevant genomic and clinical features, and the lack
of very large scale patient data repositories make this a unique challenge.
"Pure data" approaches to this problem are underpowered to detect
combinatorially complex interactions and are bound to uncover false
correlations despite statistical precautions taken (1). To investigate this
setting, we propose a method to integrate simulations, a strong form of prior
knowledge, into machine learning, a combination which to date has been largely
unexplored. The results of multiple simulations (under various uncertainty
scenarios) are used to compute similarity measures between every pair of
samples: sample pairs are given a high similarity score if they behave
similarly under a wide range of simulation parameters. These similarity values,
rather than the original high dimensional feature data, are used to train
kernelized machine learning algorithms such as support vector machines, thus
handling the curse-of-dimensionality that typically affects genomic machine
learning. Using four synthetic datasets of complex systems--three biological
models and one network flow optimization model--we demonstrate that when the
number of training samples is small compared to the number of features, the
simulation kernel approach dominates over no-prior-knowledge methods. In
addition to biology and medicine, this approach should be applicable to other
disciplines, such as weather forecasting, financial markets, and agricultural
management, where predictive models are sought and informative yet approximate
simulations are available. The Python SimKern software, the models (in MATLAB,
Octave, and R), and the datasets are made freely available at
this https URL .