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

INTEGRATIVE ANALYSIS FOR LUNG ADENOCARCINOMA PREDICTS MORPHOLOGICAL FEATURES ASSOCIATED WITH GENETIC VARIATIONS.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing - 1 Jan 2017

Wang Chao, Su Hai, Yang Lin, Huang Kun

Abstract excerpt

Lung cancer is one of the most deadly cancers and lung adenocarcinoma (LUAD) is the most common histological type of lung cancer. However, LUAD is highly heterogeneous due to genetic difference as well as phenotypic differences such as cellular and tissue morphology. In this paper, we systematically examine the relationships between histological features and gene transcription. Specifically, we calculated 283...

Read the complete abstract on PubMed

Topics

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

Share to Adenocarcinoma
Share to Adenocarcinoma of Lung

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Algorithms

Share to Biomarkers, Tumor

Share to Computational Biology

Share to Gene Regulatory Networks

Share to Multigene Family

Share to Regression Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 27896964
DOI: 10.1142/9789813207813_0009
PMCID: PMC5360185

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

INTEGRATIVE ANALYSIS FOR LUNG ADENOCARCINOMA PREDICTS MORPHOLOGICAL FEATURES ASSOCIATED WITH GENETIC VARIATIONS.PMID 27896964Read ↗

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinomaSemantic proximity Read ↗

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell CarcinomaSemantic proximity Read ↗

Comprehensive profiling of genomic and transcriptomic differences between risk groups of lung adenocarcinoma and lung squamous cell carcinomaSemantic proximity Read ↗

Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinomaSemantic proximity Read ↗

Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinomaSemantic proximity Read ↗

Multi‐omics analyses reveal distinct molecular characteristics and transformation mechanisms of stage I– III micropapillary lung adenocarcinomaSemantic proximity Read ↗

Lung Cancer Genomic SignaturesSemantic proximity Read ↗

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinationsSemantic proximity Read ↗

Correlation between CT based radiomics features and gene expression data in non-small cell lung cancerSemantic proximity Read ↗

Molecular differences across invasive lung adenocarcinoma morphological subgroupsSemantic proximity Read ↗

Application of Machine Learning Models Based on H&E Staining for Mitochondrial-Related Genes Classification and Prognosis of Lung AdenocarcinomaSemantic proximity Read ↗

Clinically Relevant Characterization of Lung Adenocarcinoma Subtypes Based on Cellular Pathways: An International Validation StudySemantic proximity Read ↗

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