  Reliable identification of molecular biomarkers is essential for accurate
patient stratification. While state-of-the-art machine learning approaches for
sample classification continue to push boundaries in terms of performance, most
of these methods are not able to integrate different data types and lack
generalization power, limiting their application in a clinical setting.
Furthermore, many methods behave as black boxes, and we have very little
understanding about the mechanisms that lead to the prediction. While
opaqueness concerning machine behaviour might not be a problem in deterministic
domains, in health care, providing explanations about the molecular factors and
phenotypes that are driving the classification is crucial to build trust in the
performance of the predictive system. We propose Pathway Induced Multiple
Kernel Learning (PIMKL), a novel methodology to reliably classify samples that
can also help gain insights into the molecular mechanisms that underlie the
classification. PIMKL exploits prior knowledge in the form of a molecular
interaction network and annotated gene sets, by optimizing a mixture of
pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an
approach that has demonstrated excellent performance in different machine
learning applications. After optimizing the combination of kernels for
prediction of a specific phenotype, the model provides a stable molecular
signature that can be interpreted in the light of the ingested prior knowledge
and that can be used in transfer learning tasks.
