ArticleBioinformatics (Oxford, England)2014
A pathway-based data integration framework for prediction of disease progression.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Integrating multi-omics data: Methods and applications in human complex diseases.Biotechnology reports (Amsterdam, Netherlands) · 2025Review
- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- Priority-Elastic net for binary disease outcome prediction based on multi-omics data.BioData mining · 2024Article
- MoNETA: MultiOmics Network Embedding for SubType Analysis.NAR genomics and bioinformatics · 2024Article
- Invention of 3Mint for feature grouping and scoring in multi-omics.Frontiers in genetics · 2023Article
- A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis.BMC medical genomics · 2022Article
- A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies.Journal of the American Statistical Association · 2022Article
- Article
- PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.PLoS computational biology · 2021Article
- Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.PLoS computational biology · 2021Article
- A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.International journal of molecular sciences · 2021Review
- Machine learning analysis of TCGA cancer data.PeerJ. Computer science · 2021Article
- MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks.PeerJ. Computer science · 2021Article
- Integrative analysis of DNA methylation and gene expression in papillary renal cell carcinoma.Molecular genetics and genomics : MGG · 2020Article
- Comparison of Outlier-Tolerant Models for Measuring Visual Complexity.Entropy (Basel, Switzerland) · 2020Article
- Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.Briefings in bioinformatics · 2020Review
- Multi-omics Data Integration, Interpretation, and Its Application.Bioinformatics and biology insights · 2020Review
- Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods.Frontiers in genetics · 2020Article
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Authors and funding
4 authors.
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Abstract
motivationWithin medical research there is an increasing trend toward deriving multiple types of data from the same individual. The most effective prognostic prediction methods should use all available data, as this maximizes the amount of information used. In this article, we consider a variety of learning strategies to boost prediction performance based on the use of all available data. IMPLEMENTATION: We consider data integration via the use of multiple kernel learning supervised learning methods. We propose a scheme in which feature selection by statistical score is performed separately per data type and by pathway membership. We further consider the introduction of a confidence measure for the class assignment, both to remove some ambiguously labeled datapoints from the training data and to implement a cautious classifier that only makes predictions when the associated confidence is high.
resultsWe use the METABRIC dataset for breast cancer, with prediction of survival at 2000 days from diagnosis. Predictive accuracy is improved by using kernels that exclusively use those genes, as features, which are known members of particular pathways. We show that yet further improvements can be made by using a range of additional kernels based on clinical covariates such as Estrogen Receptor (ER) status. Using this range of measures to improve prediction performance, we show that the test accuracy on new instances is nearly 80%, though predictions are only made on 69.2% of the patient cohort. AVAILABILITY: https://github.com/jseoane/FSMKL CONTACT: J.Seoane@bristol.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.