PIMKL: Pathway Induced Multiple Kernel Learning

TL;DR

PIMKL is a novel machine learning method that integrates molecular interaction networks and gene sets to classify samples accurately and interpretably, aiding biomarker discovery and understanding molecular mechanisms.

Contribution

It introduces PIMKL, a new approach combining prior biological knowledge with multiple kernel learning for improved, interpretable sample classification in healthcare.

Findings

Achieves high classification accuracy across datasets.

Provides interpretable molecular signatures.

Enables transfer learning with stable signatures.

Abstract

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.