ArticleNPJ systems biology and applications2019
PIMKL: Pathway-Induced Multiple Kernel Learning.
Article in NPJ systems biology and applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Interpretable and integrative analysis of single-cell multiomics with scMKL.Communications biology · 2025Article
- AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.Computational and structural biotechnology journal · 2025Review
- COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms.PLoS computational biology · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer.Scientific reports · 2023Article
- COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.Bioinformatics (Oxford, England) · 2023Article
- Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.PLoS computational biology · 2021Article
- COSIFER: a Python package for the consensus inference of molecular interaction networks.Bioinformatics (Oxford, England) · 2021Article
- Fenchel duality of Cox partial likelihood with an application in survival kernel learning.Artificial intelligence in medicine · 2021Article
- Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.PLoS computational biology · 2021Article
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- Incorporating biological structure into machine learning models in biomedicine.Current opinion in biotechnology · 2020Review
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4 authors.
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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 behavior 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 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 to predict 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.
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