Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.

Bioinformatics (Oxford, England) - 1 Mar 2020

Li Jun, Lu Qing, Wen Yalu

Abstract excerpt

MOTIVATION: The 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. RESULTS: We present a multi-kernel penalized linear mixed model with...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Genomics
Share to Linear Models

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Precision Medicine

Source

Open in PubMed Open full text

Identifiers

PMID: 31693075
DOI: 10.1093/bioinformatics/btz822
PMCID: PMC7523642

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.PMID 31693075Read ↗

Multikernel linear mixed model with adaptive lasso for complex phenotype prediction.Semantic proximity Read ↗

A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction.Semantic proximity Read ↗

Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.Semantic proximity Read ↗

Multikernel linear mixed models for complex phenotype prediction.Semantic proximity Read ↗

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.Semantic proximity Read ↗

Identification of functionally connected multi-omic biomarkers for Alzheimer’s Disease using modularity-constrained LassoSemantic proximity Read ↗

Integrative machine learning approaches for predicting disease risk using multi-omics data from the UK BiobankSemantic proximity Read ↗

Joint Variable Selection for Omic Biomarkers in Time-to-Event DataSemantic proximity Read ↗

Identification of functionally connected multi-omic biomarkers for Alzheimer's disease using modularity-constrained Lasso.Semantic proximity Read ↗

Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic ProfilesSemantic proximity Read ↗

Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.Semantic proximity Read ↗

Translational bioinformatics and machine learning framework for biomarker discovery, disease prediction, and patient profiling for precision medicineSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.