ArticleBioinformatics (Oxford, England)2020
Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models.Entropy (Basel, Switzerland) · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.Computational and structural biotechnology journal · 2025Review
- ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations.Computers in biology and medicine · 2024Article
- Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2023Article
- A guide to multi-omics data collection and integration for translational medicine.Computational and structural biotechnology journal · 2023Review
- A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.Briefings in bioinformatics · 2022Article
- Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.PLoS computational biology · 2022Article
- Review
- Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis.BMC medical genomics · 2022Article
- The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease.Frontiers in aging neuroscience · 2022Review
- Microbial communities in the liver and brain are informative for postmortem submersion interval estimation in the late phase of decomposition: A study in mouse cadavers recovered from freshwater.Frontiers in microbiology · 2022Article
- PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning.BMC bioinformatics · 2021Article
- Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data.Journal of personalized medicine · 2021Article
- Integrating Multi-Omics Data for Gene-Environment Interactions.Biotech (Basel (Switzerland)) · 2021Article
- Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study.Genes · 2019Article
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Authors and funding
3 authors.
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Abstract
motivationThe use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed.
resultsWe present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer's Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/YaluWen/OmicPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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