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

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.

PLoS genetics - 1 Dec 2015

Pineda Silvia, Real Francisco X, Kogevinas Manolis, Carrato Alfredo, Chanock Stephen J, Malats Núria, Van Steen Kristel

Abstract excerpt

Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to CpG Islands

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to DNA Methylation

Share to Gene Expression Regulation, Neoplastic

Share to Genomics

Share to Neoplasm Proteins

Share to Software

Share to Urinary Bladder Neoplasms

Source

Open in PubMed Open full text

Identifiers

PMID: 26646822
DOI: 10.1371/journal.pgen.1005689
PMCID: PMC4672920

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

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

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.PMID 26646822Read ↗

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

Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases.Semantic proximity Read ↗

Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk.Semantic proximity Read ↗

conMItion: an R package adjusting confounding factors for associations in multi-omicsSemantic proximity Read ↗

A screening-testing approach for detecting gene-environment interactions using sequential penalized and unpenalized multiple logistic regression.Semantic proximity Read ↗

Whole genome prediction of bladder cancer risk with the Bayesian LASSO.Semantic proximity Read ↗

Integrated multi-omics analyses reveal causal insights into the molecular landscape of urologic cancersSemantic proximity Read ↗

Integrative Approaches to Reconstruct Regulatory Networks From Multi-Omics Data: A Review of State-of-the-Art MethodsSemantic proximity Read ↗

Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancerSemantic proximity Read ↗

The Construction of Polygenic Risk Scores for Breast Cancer Based on LightGBM and Multiple Omics DataSemantic proximity Read ↗

General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals.Semantic proximity Read ↗

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

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