دورية أكاديمية
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Evaluation via Supervised Machine Learning of the Broiler Pectoralis Major and Liver Transcriptome in Association With the Muscle Myopathy Wooden Breast.

التفاصيل البيبلوغرافية
العنوان: Evaluation via Supervised Machine Learning of the Broiler Pectoralis Major and Liver Transcriptome in Association With the Muscle Myopathy Wooden Breast.
المؤلفون: Phillips CA; Prestage Department of Poultry Science, North Carolina State University, Raleigh, NC, United States., Reading BJ; Department of Applied Ecology, North Carolina State University, Raleigh, NC, United States., Livingston M; Prestage Department of Poultry Science, North Carolina State University, Raleigh, NC, United States., Livingston K; Prestage Department of Poultry Science, North Carolina State University, Raleigh, NC, United States., Ashwell CM; Prestage Department of Poultry Science, North Carolina State University, Raleigh, NC, United States.
المصدر: Frontiers in physiology [Front Physiol] 2020 Feb 25; Vol. 11, pp. 101. Date of Electronic Publication: 2020 Feb 25 (Print Publication: 2020).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101549006 Publication Model: eCollection Cited Medium: Print ISSN: 1664-042X (Print) Linking ISSN: 1664042X NLM ISO Abbreviation: Front Physiol Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Lausanne : Frontiers Research Foundation
مستخلص: The muscle myopathy wooden breast (WB) has recently appeared in broiler production and has a negative impact on meat quality. WB is described as hard/firm consistency found within the pectoralis major (PM). In the present study, we use machine learning from our PM and liver transcriptome dataset to capture the complex relationships that are not typically revealed by traditional statistical methods. Gene expression data was evaluated between the PM and liver of birds with WB and those that were normal. Two separate machine learning algorithms were performed to analyze the data set including the sequential minimal optimization (SMO) of support vector machines (SVMs) and Multilayer Perceptron (MLP) Artificial Neural Network (ANN). Machine learning algorithms were compared to identify genes within a gene expression data set of approximately 16,000 genes for both liver and PM, which can be correctly classified from birds with or without WB. The performance of both machine learning algorithms SMO and MLP was determined using percent correct classification during the cross-validations. By evaluating the WB transcriptome datasets by 5× cross-validation using ANNs, the expression of nine genes ranked based on Shannon Entropy (Information Gain) from PM were able to correctly classify if the individual bird was normal or exhibited WB 100% of the time. These top nine genes were all protein coding and potential biomarkers. When PM gene expression data were evaluated between normal birds and those with WB using SVMs they were correctly classified 95% of the time using 450 of the top genes sorted ranked based on Shannon Entropy (Information Gain) as a preprocessing step. When evaluating the 450 attributes that were 95% correctly classified using SVMs through Ingenuity Pathway Analysis (IPA) there was an overlap in top genes identified through MLP. This analysis allowed the identification of critical transcriptional responses for the first time in both liver and muscle during the onset of WB. The information provided has revealed many molecules and pathways making up a complex molecular mechanism involved with the progression of wooden breast and suggests that the etiology of the myopathy is not limited to activity in the muscle alone, but is an altered systemic pathology.
(Copyright © 2020 Phillips, Reading, Livingston, Livingston and Ashwell.)
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فهرسة مساهمة: Keywords: artificial neural networks; machine learning; poultry transcriptomics; support vector machines; transforming growth factor; wooden breast
تواريخ الأحداث: Date Created: 20200312 Latest Revision: 20240328
رمز التحديث: 20240329
مُعرف محوري في PubMed: PMC7052112
DOI: 10.3389/fphys.2020.00101
PMID: 32158398
قاعدة البيانات: MEDLINE
الوصف
تدمد:1664-042X
DOI:10.3389/fphys.2020.00101