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akaturk Akademik ölçüm

Makale detayı · 2026

Machine learning–driven discovery of host genetic factors for paratuberculosis in goats within the one health framework

BMC Veterinary Research

YÖKSİS OpenAlex ISSN 1746-6148 DOI 10.1186/s12917-026-05430-x Atıf 1 Açık erişim · gold SJR Q1 · 2025 JCR Q1 · 2025

10.1186/s12917-026-05430-x

YÖKSİS YÖKSİS makale kaydı

OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

OpenAlex kaydı

İngilizce (OpenAlex)

Paratuberculosis, caused by Mycobacterium avium subsp. paratuberculosis (MAP), remains a persistent One Health concern due to its slow clinical course, environmental resilience, wide circulation in ruminant systems, and unresolved zoonotic implications. To characterise MAP exposure across Türkiye’s goat populations, we conducted a nationwide genomic survey encompassing seven breeds from 36 farms in 11 provinces. High-density SNP genotyping combined with mutual-information–based feature preselection retained informative, non-redundant loci capturing both linear and nonlinear components of disease architecture.Nine complementary machine-learning models were applied to identify host genetic factors underlying MAP infection, and an ensemble importance framework resolved 31 FDR-controlled SNPs consistently associated with MAP status. Functional annotation implicated immune processes including cytokine–receptor signalling, antigen presentation, glycan-mediated T-cell regulation, and NF-κB-linked inflammation. Concordance with mixed linear models and genome-wide McNemar tests suggested that both additive and non-additive genetic effects shape the observed signal. These reproducible, albeit preliminary, markers outline a genomic foundation for breeding MAP-resilient goats and point to opportunities for reducing pathogen shedding at its source within a broader One Health strategy.

OpenAlex zenginleştirmesi

Konular

  • Mycobacterium research and diagnosis
  • Tuberculosis Research and Epidemiology
  • Diphtheria, Corynebacterium, and Tetanus

Tür: article Mycobacterium research and diagnosis

İndeks bilgisi

WoS (JCR) ve Scopus (SJR) çeyrekleri ISSN ve yayın yılına göre. · 2026

Scopus (SJR) / WoS (JCR)

BMC Veterinary Research

Scopus (SJR) Q1 0,697 En yakın yıl: 2025

Makale yılı 2026; gösterilen indeks yılı 2025.

WoS (JCR) Q1 JIF 3,1 En yakın yıl: 2025

Makale yılı 2026; gösterilen indeks yılı 2025.

Üniversiteler

  • SİİRT ÜNİVERSİTESİ

Yazarlar

  1. YALÇIN YAMAN SİİRT ÜNİVERSİTESİ
  2. AHMET ESER SİİRT ÜNİVERSİTESİ
  3. DEVRAN COŞKUN
  4. Ramazan Aymaz
  5. Yiğit Emir Kişi
  6. Murat Keleş
  7. Serdar Yağcı
  8. ÖZGÜL GÜLAYDIN SİİRT ÜNİVERSİTESİ
  9. Serkan Süleyman Şengül
  10. KIVANÇ İRAK
  11. MEMİŞ BOLACALI