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

Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction.

Journal of cellular biochemistry - 1 Jan 2018

Ge Wen-Han, Lin Yong, Li Sen, Zong Xuefeng, Ge Zhong-Chun

Abstract excerpt

Acute myocardial infarction (AMI) is a common disease with serious consequences in mortality and cost. Here we aim to screen the differentially expressed genes (DEGs) as biomarkers for early diagnosis of AMI. The microarray data of AMI was downloaded from Gene Expression Omnibus (GEO), including two independent types of research GSE66360 and GSE62646. The DEGs between control and processed samples were screened...

Topics

Join the communities discussing this publication.

Biomarkers, Tumor
Computational Biology
Databases, Genetic
Early Detection of Cancer
Gene Regulatory Networks

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Myocardial Infarction

Oligonucleotide Array Sequence Analysis

Regression Analysis

Source

Identifiers

PMID: 28636181
DOI: 10.1002/jcb.26226

Genes

EGR1
SOCS3
STAB1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction.PMID 28636181Read ↗

Identifying key genes associated with acute myocardial infarctionSemantic proximity Read ↗

Identification and analysis of key genes associated with acute myocardial infarction by integrated bioinformatics methodsSemantic proximity Read ↗

Potential Biomarkers of Acute Myocardial Infarction Based on Co-Expression Network AnalysisSemantic proximity Read ↗

Identification of Key Genes and Signaling Pathways Involved in Acute Myocardial Infarction and Potential Biomarkers of Its Consequent Heart Failure Based On Bioinformatics AnalysisSemantic proximity Read ↗

Identification of Key Genes Involved in Acute Myocardial Infarction by Comparative Transcriptome AnalysisSemantic proximity Read ↗

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial InfarctionSemantic proximity Read ↗

Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker AlgorithmSemantic proximity Read ↗

Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics AnalysisSemantic proximity Read ↗

Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction.Semantic proximity Read ↗

Genes associated with inflammation and the cell cycle may serve as biomarkers for the diagnosis and prognosis of acute myocardial infarction in a Chinese populationSemantic proximity Read ↗

Predicting Diagnostic Gene Biomarkers Associated With Immune Infiltration in Patients With Acute Myocardial InfarctionSemantic proximity Read ↗

Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysisSemantic proximity Read ↗

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