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

RASEL: An Ensemble Model for Selection of Core SNPs and Its Application for Identification and Classification of Cattle Breeds.

Biochemical genetics - 1 Jun 2026

Kanaka K K, Ganguly Indrajit, Singh Sanjeev, Kuralkar S V, Dixit Satpal, Sukhija Nidhi, Goli Rangasai Chandra

Abstract excerpt

Identifying and classifying different cattle populations as per their breed and utility holds immense practical importance in effective breeding management. For accurate identification and classification of cattle breeds, a reference panel of 10 breeds, 657 identified ancestry informative markers and different machine learning classifiers were employed. To boost the accuracy of breed identification, three...

Read the complete abstract on PubMed

Topics

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

Share to Breeding
Share to Cattle

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Markers

Share to Genotyping Techniques

Share to Polymorphism, Single Nucleotide

Share to Reference Standards

Share to Species Specificity

Source

Identifiers

PMID: 40844696
DOI: 10.1007/s10528-025-11230-z

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

RASEL: An Ensemble Model for Selection of Core SNPs and Its Application for Identification and Classification of Cattle Breeds.PMID 40844696Read ↗

Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data.Semantic proximity Read ↗

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.Semantic proximity Read ↗

[Estimating genomic breed composition of individual animals using selected SNPs].Semantic proximity Read ↗

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds.Semantic proximity Read ↗

Tracing cattle breeds with principal components analysis ancestry informative SNPs.Semantic proximity Read ↗

Combined use of principal component analysis and random forests identify population-informative single nucleotide polymorphisms: application in cattle breeds.Semantic proximity Read ↗

Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers.Semantic proximity Read ↗

A low-density SNP genotyping panel for the accurate prediction of cattle breeds.Semantic proximity Read ↗

Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP.Semantic proximity Read ↗

A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle.Semantic proximity Read ↗

Genome-wide characterization of genetic diversity and breed discrimination markers in Tharparkar cattle.Semantic proximity Read ↗

SNPs with intermediate minor allele frequencies facilitate accurate breed assignment of Indian Tharparkar cattle.Semantic proximity Read ↗

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