  Machine learning has been gaining traction in recent years to meet the demand
for tools that can efficiently analyze and make sense of the ever-growing
databases of biomedical data in health care systems around the world. However,
effectively using machine learning methods requires considerable domain
expertise, which can be a barrier of entry for bioinformaticians new to
computational data science methods. Therefore, off-the-shelf tools that make
machine learning more accessible can prove invaluable for bioinformaticians. To
this end, we have developed an open source pipeline optimization tool
(TPOT-MDR) that uses genetic programming to automatically design machine
learning pipelines for bioinformatics studies. In TPOT-MDR, we implement
Multifactor Dimensionality Reduction (MDR) as a feature construction method for
modeling higher-order feature interactions, and combine it with a new expert
knowledge-guided feature selector for large biomedical data sets. We
demonstrate TPOT-MDR's capabilities using a combination of simulated and real
world data sets from human genetics and find that TPOT-MDR significantly
outperforms modern machine learning methods such as logistic regression and
eXtreme Gradient Boosting (XGBoost). We further analyze the best pipeline
discovered by TPOT-MDR for a real world problem and highlight TPOT-MDR's
ability to produce a high-accuracy solution that is also easily interpretable.
