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 of multi-omics data for survival prediction of lung adenocarcinoma.

Computer methods and programs in biomedicine - 1 Jun 2024

Guo Dingjie, Wang Yixian, Chen Jing, Liu Xin

Abstract excerpt

BACKGROUND AND OBJECTIVE: The morbidity of lung adenocarcinoma (LUAD) has been increasing year by year and the prognosis is poor. This has prompted researchers to study the survival of LUAD patients to ensure that patients can be cured in time or survive after appropriate treatment. There is still no fully valid model that can be applied to clinical practice. METHODS: We introduced struc2vec-based multi-omics...

Read the complete abstract on PubMed

Topics

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

Share to Adenocarcinoma of Lung
Share to Algorithms
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Expression Regulation, Neoplastic

Share to Gene Regulatory Networks

Share to Multiomics

Share to Protein Interaction Maps

Share to Survival Analysis

Source

Identifiers

PMID: 38701699
DOI: 10.1016/j.cmpb.2024.108192

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Integration of multi-omics data for survival prediction of lung adenocarcinoma.PMID 38701699Read ↗

Survival prediction and molecular subtyping of squamous cell lung cancer based on network embedding.Semantic proximity Read ↗

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.Semantic proximity Read ↗

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung CancerSemantic proximity Read ↗

Screening key lncRNAs for human lung adenocarcinoma based on machine learning and weighted gene co-expression network analysisSemantic proximity Read ↗

Integrated Multi-Omics Analysis Identifies Key Genes and Drug Candidates in Non-Small Cell Lung CancerSemantic proximity Read ↗

Integrated Multi-Omics Analysis of Lung Adenocarcinoma (TCGA-LUAD): A Comprehensive Study of Genomic, Epigenomic, Copy Number Variations, and Transcriptomic AlterationsSemantic proximity Read ↗

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer SurvivalSemantic proximity Read ↗

Identification of Biomarker Genes Based on Multi-Omics Analysis in Non- Small Cell Lung CancerSemantic proximity Read ↗

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinomaSemantic proximity Read ↗

MDICC: novel method for multi-omics data integration and cancer subtype identificationSemantic proximity Read ↗

Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.Semantic proximity Read ↗

Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics AnalysisSemantic proximity Read ↗

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