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

Robust and stable gene selection via Maximum-Minimum Correntropy Criterion.

Genomics - 1 Mar 2016

Mohammadi Majid, Sharifi Noghabi Hossein, Abed Hodtani Ghosheh, Rajabi Mashhadi Habib

Abstract excerpt

One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this article, we propose a Maximum-Minimum Correntropy Criterion (MMCC) approach for selection of...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology
Share to Gene Expression Profiling

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to Neoplasms

Share to Pattern Recognition, Automated

Share to Support Vector Machine

Source

Identifiers

PMID: 26762945
DOI: 10.1016/j.ygeno.2015.12.006

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Robust and stable gene selection via Maximum-Minimum Correntropy Criterion.PMID 26762945Read ↗

A robust and stable gene selection algorithm based on graph theory and machine learning.Semantic proximity Read ↗

Efficient feature selection on gene expression data: Which algorithm to use?Semantic proximity Read ↗

New gene selection method for classification of cancer subtypes considering within-class variation.Semantic proximity Read ↗

Mutation-based Binary Aquila optimizer for gene selection in cancer classification.Semantic proximity Read ↗

RSGSA: a Robust and Stable Gene Selection AlgorithmSemantic proximity Read ↗

Semi-Supervised Maximum Discriminative Local Margin for Gene Selection.Semantic proximity Read ↗

Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study.Semantic proximity Read ↗

Cancer Classification through Gene Selection Using the Social Spider Optimization AlgorithmSemantic proximity Read ↗

Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq Samples.Semantic proximity Read ↗

FM-GA and CM-GA for gene microarray analysis.Semantic proximity Read ↗

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.Semantic proximity Read ↗

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.Semantic proximity Read ↗

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