ArticleBMC bioinformatics2018
Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Profile-guided Hybrid Approach for block-wise missing data handling in multi-omics: a breast cancer case study.BioData mining · 2026Article
- Integration of clinical and proteomic risk factors enhances prognostic modelling of incident vascular complications in type 2 diabetes.Cardiovascular diabetology · 2026Article
- A clustering-stratified cross-validation framework for validating omics survival models: application to head and neck cancer.BMC medical research methodology · 2025Article
- SurvBoard: standardized benchmarking for multi-omics cancer survival models.Briefings in bioinformatics · 2025Article
- Pharmacogenomics of Tirzepatide: Genomic Insights into Dual GIP/GLP-1 Agonist Response in Type 2 Diabetes and Atherosclerosis.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort.Diabetes, obesity & metabolism · 2025Article
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- High-Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Internal validation strategy for high dimensional prognosis model: A simulation study and application to transcriptomic in head and neck tumors.Computational and structural biotechnology journal · 2025Article
- Meta-analysis of multi-center transcriptomic profiles and machine learning reveal phospholipase Cβ4 as a Wnt/Ca²Frontiers in immunology · 2025Article
- Priority-Elastic net for binary disease outcome prediction based on multi-omics data.BioData mining · 2024Article
- Article
- Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.BMC medical informatics and decision making · 2024Article
- Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank.Diabetologia · 2024Article
- Relationships and Mendelian Randomization of Gut Microbe-Derived Metabolites with Metabolic Syndrome Traits in the METSIM Cohort.Metabolites · 2024Article
- Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study.Cardiovascular diabetology · 2024Article
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5 authors.
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Abstract
backgroundThe inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind.
resultsIn this paper we present priority-Lasso, an intuitive and practical analysis strategy for building prediction models based on Lasso that takes such block structures into account. It requires the definition of a priority order of blocks of data. Lasso models are calculated successively for every block and the fitted values of every step are included as an offset in the fit of the next step. We apply priority-Lasso in different settings on an acute myeloid leukemia (AML) dataset consisting of clinical variables, cytogenetics, gene mutations and expression variables, and compare its performance on an independent validation dataset to the performance of standard Lasso models.
conclusionThe results show that priority-Lasso is able to keep pace with Lasso in terms of prediction accuracy. Variables of blocks with higher priorities are favored over variables of blocks with lower priority, which results in easily usable and transportable models for clinical practice.
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