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

Detecting pathway biomarkers of diabetic progression with differential entropy.

Journal of biomedical informatics - 1 Jun 2018

Liu Zhi-Ping, Gao Rui

Abstract excerpt

Gene expression profiling techniques measure the transcriptional dynamics of thousands of genes in parallel manners. The available high-throughput transcriptomic datasets provide unprecedented opportunities of detecting biomarkers or signatures of complex diseases such as diabetes. In this work, we propose a computational method based on differential entropy to identify diabetic pathway biomarkers in rats from...

Read the complete abstract on PubMed

Topics

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

Share to Area Under Curve
Share to Entropy
Share to Liver

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Medical Informatics

Share to Rats, Inbred WKY

Share to Sphingolipids

Share to Transcriptome

Source

Identifiers

PMID: 29763705
DOI: 10.1016/j.jbi.2018.05.006

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Detecting pathway biomarkers of diabetic progression with differential entropy.PMID 29763705Read ↗

Identifying the critical states and dynamic network biomarkers of cancers based on network entropySemantic proximity Read ↗

An Entropy-based Directed Random Walk for Pathway Activity Inference Using Topological Importance and Gene InteractionsSemantic proximity Read ↗

Extracting Genes Involved in Disease from a Connected Network of Perturbed Biological Processes.Semantic proximity Read ↗

Investigation of gene expression and genetic simultaneous control associated with erectile dysfunction and diabetesSemantic proximity Read ↗

NOGEA: Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug RepositioningSemantic proximity Read ↗

Differential network analysis between sex of thegenes related to comorbidities of type 2 mellitusdiabetes.Semantic proximity Read ↗

Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.Semantic proximity Read ↗

Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkersSemantic proximity Read ↗

Biomarkers for capturing disease pathology as molecular process hyperstructureSemantic proximity Read ↗

Gene Expression Network Analysis (GENA) – A Biostatistical Method to Unravel Intricate Relationships Between GenesSemantic proximity Read ↗

LAMP: disease classification derived from layered assessment on modules and pathways in the human gene networkSemantic proximity Read ↗

Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networksSemantic proximity Read ↗

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