Skip to main content

ORIGINAL RESEARCH article

Front. Vet. Sci.
Sec. Animal Behavior and Welfare
Volume 11 - 2024 | doi: 10.3389/fvets.2024.1508397
This article is part of the Research Topic Vetinformatics: An Insight for Decoding Livestock Systems Through In Silico Biology Volume II View all 5 articles

Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA

Provisionally accepted
Jiying Wen Jiying Wen Shenglin Yang Shenglin Yang *Jinjin Zhu Jinjin Zhu Ai Liu Ai Liu Qisong Tan Qisong Tan Yifu Rao Yifu Rao
  • Guizhou University, Guiyang, China

The final, formatted version of the article will be published soon.

    Feather pecking (FP) is a significant welfare concern in poultry, which can result in reduced egg production, deterioration of feather condition, and an increase in mortality rate. This can harm the health of birds and the economic benefits of breeders. FP, as a complex trait, is regulated by multiple factors, and so far, no one has been able to elucidate its exact mechanism. In order to delve deeper into the genetic mechanism of FP, we acquired the expression matrix of dataset GSE36559. We analyzed the gene modules associated with the trait through WGCNA (Weighted Correlation Network Analysis) weighted gene co expression network, and then used KEGG and GO to identify the biological pathways enriched by the modules using KEGG and GO. Subsequently, we analyzed the module with the highest correlation (0.99) using three machine learning (ML) algorithms to identify the feature genes that they collectively recognized. In this study, five feature genes, NUFIP2, ST14, OVM, GLULD1, and LOC424943, were identified. Finally, the discriminant value of the feature genes was evaluated by manipulating the receiver operating curve (ROC) in the external dataset GSE10380.

    Keywords: Machine Learing, feather pecking, pathological behavior, WGCNA, bioinformatics

    Received: 09 Oct 2024; Accepted: 18 Nov 2024.

    Copyright: © 2024 Wen, Yang, Zhu, Liu, Tan and Rao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shenglin Yang, Guizhou University, Guiyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.