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 URL .
Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning , which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines . 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 build the kernel. Results: We demonstrate and explore the simulation based kernel (SimKern) concept using four synthetic complex systems--three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available. Availability: The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at URL 
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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
Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning
R
a combination which to date has been largely unexplored
which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines
R
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
build the kernel. Results: We demonstrate and explore the simulation based kernel (SimKern) concept using four synthetic
R
biological
biologically inspired
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model--we demonstrate that
model. We show that,
R
simulation kernel
SimKern
R
In addition to biology and medicine, this
This
R
to other disciplines , such as weather forecasting, financial markets, and agricultural management,
in all disciplines
A
Availability:
A
demonstration
D
Octave, and
D
made freely
D
.