Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA
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 multip...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2024.1508397/full |
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| author | Jiying Wen Shenglin Yang Jinjin Zhu Ai Liu Qisong Tan Yifu Rao |
| author_facet | Jiying Wen Shenglin Yang Jinjin Zhu Ai Liu Qisong Tan Yifu Rao |
| author_sort | Jiying Wen |
| collection | DOAJ |
| description | 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), 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. |
| format | Article |
| id | doaj-art-3da668eff3d0448e8496ec6c3eec4fa1 |
| institution | DOAJ |
| issn | 2297-1769 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj-art-3da668eff3d0448e8496ec6c3eec4fa12025-08-20T02:48:54ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692024-11-011110.3389/fvets.2024.15083971508397Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNAJiying WenShenglin YangJinjin ZhuAi LiuQisong TanYifu RaoFeather 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), 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.https://www.frontiersin.org/articles/10.3389/fvets.2024.1508397/fullmachine learingfeather peckingpathological behaviorWGCNAbioinformatics |
| spellingShingle | Jiying Wen Shenglin Yang Jinjin Zhu Ai Liu Qisong Tan Yifu Rao Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA Frontiers in Veterinary Science machine learing feather pecking pathological behavior WGCNA bioinformatics |
| title | Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA |
| title_full | Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA |
| title_fullStr | Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA |
| title_full_unstemmed | Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA |
| title_short | Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA |
| title_sort | identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and wgcna |
| topic | machine learing feather pecking pathological behavior WGCNA bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2024.1508397/full |
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