2016/01/29 by Mar Rodríguez‐Girondo, Mar Rodríguez-Girondo, Rodríguez-Girondo, Mar +11
Biochemistry, Genetics and Molecular Biology · Mathematics · #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME) #stat.ME
paper · pdf · doi:10.48550/arxiv.1601.08197
openalex publication_date 2016/01/29 · arxiv created 2016/10/17 · arxiv updated 2016/10/18 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
Enriching existing predictive models with new biomolecular markers is an important task in the new multi-omic era. Clinical studies increasingly include new sets of omic measurements which may prove their added value in terms of predictive performance. We introduce a two-step approach for the assessment of the added predictive ability of omic predictors, based on sequential double cross-validation and regularized regression models. We propose several performance indices to summarize the two-stage prediction procedure and a permutation test to formally assess the added predictive value of a second omic set of predictors over a primary omic source. The performance of the test is investigated through simulations. We illustrate the new method through the systematic assessment and comparison of the performance of transcriptomics and metabolomics sources in the prediction of body mass index (BMI) using longitudinal data from the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort from Finland.