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

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

1 Dec 2020

Abstract excerpt

BACKGROUND: Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two...

Read the complete abstract on PubMed

Topics

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

Share to Bioinformatics and Genomic Networks
Share to Clusterin in disease pathology
Share to Gene expression and cancer classification

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 33272232
DOI: 10.1186/s12859-020-3524-8

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studiesPMID 33272232Read ↗

Screening of biomarkers for lung cancer with gene expression profiling data.Semantic proximity Read ↗

Identification of Biomarkers and Enriched Pathways Involved in Lung CancerSemantic proximity Read ↗

The identification of key biomarkers in patients with lung adenocarcinoma based on bioinformaticsSemantic proximity Read ↗

Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinomaSemantic proximity Read ↗

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning ClassifierSemantic proximity Read ↗

Biomarkers and transcriptome profiling of lung cancer.Semantic proximity Read ↗

Identification of Biomarker Signatures and Candidate Drugs in Non-Small Cell Lung CancerSemantic proximity Read ↗

Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approachesSemantic proximity Read ↗

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning MethodSemantic proximity Read ↗

Exploration of Prognostic Biomarkers for Lung Adenocarcinoma Through Bioinformatics AnalysisSemantic proximity Read ↗

Integrated multi-omic analysis and experiment reveals the role of endoplasmic reticulum stress in lung adenocarcinomaSemantic proximity Read ↗

Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancerSemantic proximity Read ↗

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