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Hou G, Li R, Tian M, Ding J, Zhang X, Yang B, Chen C, Huang R, Yin Y. Improving Efficiency: Automatic Intelligent Weighing System as a Replacement for Manual Pig Weighing. Animals (Basel) 2024; 14:1614. [PMID: 38891661 PMCID: PMC11171250 DOI: 10.3390/ani14111614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
To verify the accuracy of AIWS, we weighed 106 pen growing-finishing pigs' weights using both the manual and AIWS methods, respectively. Accuracy was evaluated based on the values of MAE, MAPE, and RMSE. In the growth experiment, manual weighing was conducted every two weeks and AIWS predicted weight data was recorded daily, followed by fitting the growth curves. The results showed that MAE, MAPE, and RMSE values for 60 to 120 kg pigs were 3.48 kg, 3.71%, and 4.43 kg, respectively. The correlation coefficient r between the AIWS and manual method was 0.9410, and R2 was 0.8854. The two were extremely significant correlations (p < 0.001). In growth curve fitting, the AIWS method has lower AIC and BIC values than the manual method. The Logistic model by AIWS was the best-fit model. The age and body weight at the inflection point of the best-fit model were 164.46 d and 93.45 kg, respectively. The maximum growth rate was 831.66 g/d. In summary, AIWS can accurately predict pigs' body weights in actual production and has a better fitting effect on the growth curves of growing-finishing pigs. This study suggested that it was feasible for AIWS to replace manual weighing to measure the weight of 50 to 120 kg live pigs in large-scale farming.
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Affiliation(s)
- Gaifeng Hou
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Hunan Research Center of Livestock and Poultry Sciences, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, National Engineering Laboratory for Poultry Breeding Pollution Control and Resource Technology, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; (G.H.); (R.L.); (M.T.); (J.D.)
| | - Rui Li
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Hunan Research Center of Livestock and Poultry Sciences, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, National Engineering Laboratory for Poultry Breeding Pollution Control and Resource Technology, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; (G.H.); (R.L.); (M.T.); (J.D.)
| | - Mingzhou Tian
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Hunan Research Center of Livestock and Poultry Sciences, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, National Engineering Laboratory for Poultry Breeding Pollution Control and Resource Technology, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; (G.H.); (R.L.); (M.T.); (J.D.)
| | - Jing Ding
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Hunan Research Center of Livestock and Poultry Sciences, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, National Engineering Laboratory for Poultry Breeding Pollution Control and Resource Technology, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; (G.H.); (R.L.); (M.T.); (J.D.)
| | - Xingfu Zhang
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin 150050, China;
- Beijing Focused Loong Technology Co., Ltd., Beijing 100086, China
| | - Bin Yang
- Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
| | - Chunyu Chen
- College of Information and Communication, Harbin Engineering University, Harbin 150001, China;
| | - Ruilin Huang
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Hunan Research Center of Livestock and Poultry Sciences, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, National Engineering Laboratory for Poultry Breeding Pollution Control and Resource Technology, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; (G.H.); (R.L.); (M.T.); (J.D.)
| | - Yulong Yin
- CAS Key Laboratory of Agro-Ecological Processes in Subtropical Region, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Hunan Research Center of Livestock and Poultry Sciences, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, National Engineering Laboratory for Poultry Breeding Pollution Control and Resource Technology, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; (G.H.); (R.L.); (M.T.); (J.D.)
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Wang Y, Wang Z, Liu Y, Yu Q, Liu Y, Luo C, Wang S, Liu H, Liu M, Zhang G, Fan Y, Li K, Huang L, Duan M, Zhou F. Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality. BMC Infect Dis 2023; 23:622. [PMID: 37735372 PMCID: PMC10514938 DOI: 10.1186/s12879-023-08291-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 04/28/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts. METHODS We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients. RESULTS Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values. CONCLUSIONS Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php .
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Affiliation(s)
- Yueying Wang
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, 130021, Changchun, Jilin Province, China
| | - Zhao Wang
- College of Software, Jilin University, 130012, Changchun, China
| | - Yaqing Liu
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, 130021, Changchun, Jilin Province, China
| | - Yujia Liu
- College of Software, Jilin University, 130012, Changchun, China
| | - Changfan Luo
- College of Software, Jilin University, 130012, Changchun, China
| | - Siyang Wang
- College of Software, Jilin University, 130012, Changchun, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Mingyou Liu
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Yusi Fan
- College of Software, Jilin University, 130012, Changchun, China
| | - Kewei Li
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Lan Huang
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Meiyu Duan
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China.
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China.
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China.
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Guevara L, Castro-Espinoza F, Fernandes AM, Benaouda M, Muñoz-Benítez AL, del Razo-Rodríguez OE, Peláez-Acero A, Angeles-Hernandez JC. Application of Machine Learning Algorithms to Describe the Characteristics of Dairy Sheep Lactation Curves. Animals (Basel) 2023; 13:2772. [PMID: 37685036 PMCID: PMC10487024 DOI: 10.3390/ani13172772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting and modeling complex data. Therefore, the aim of this study was to evaluate the prediction ability of different ML algorithms and a traditional empirical model to estimate the parameters of lactation curves. A total of 1186 monthly records from 156 sheep lactations were used. The model development process involved training and testing models using ML algorithms. In addition to these algorithms, lactation curves were also fitted using the Wood model. The goodness of fit was assessed using correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and relative root mean square error (RRSE). SMOreg was the algorithm with the best estimates of the characteristics of the sheep lactation curve, with higher values of r compared to the Wood model (0.96 vs. 0.68) for the total milk yield. The results of the current study showed that ML algorithms are able to adequately predict the characteristics of the lactation curve, using a relatively small number of input data. Some ML algorithms provide an interpretable architecture, which is useful for decision-making at the farm level to maximize the use of available information.
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Affiliation(s)
- Lilian Guevara
- Centro de Ciências e Tecnologias Agropecuárias, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes 28013-620, Brazil; (L.G.); (A.M.F.)
| | - Félix Castro-Espinoza
- Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Pachuca 42184, Mexico;
| | - Alberto Magno Fernandes
- Centro de Ciências e Tecnologias Agropecuárias, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes 28013-620, Brazil; (L.G.); (A.M.F.)
| | | | - Alfonso Longinos Muñoz-Benítez
- Instituto de Ciencias Agropecuarias, Universidad Autónoma del Estado de Hidalgo, Tulancingo de Bravo 43600, Mexico; (A.L.M.-B.); (O.E.d.R.-R.); (A.P.-A.)
| | - Oscar Enrique del Razo-Rodríguez
- Instituto de Ciencias Agropecuarias, Universidad Autónoma del Estado de Hidalgo, Tulancingo de Bravo 43600, Mexico; (A.L.M.-B.); (O.E.d.R.-R.); (A.P.-A.)
| | - Armando Peláez-Acero
- Instituto de Ciencias Agropecuarias, Universidad Autónoma del Estado de Hidalgo, Tulancingo de Bravo 43600, Mexico; (A.L.M.-B.); (O.E.d.R.-R.); (A.P.-A.)
| | - Juan Carlos Angeles-Hernandez
- Instituto de Ciencias Agropecuarias, Universidad Autónoma del Estado de Hidalgo, Tulancingo de Bravo 43600, Mexico; (A.L.M.-B.); (O.E.d.R.-R.); (A.P.-A.)
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Forutan M, Lynn A, Aliloo H, Clark SA, McGilchrist P, Polkinghorne R, Hayes BJ. Predicting phenotypes of beef eating quality traits. Front Genet 2023; 14:1089490. [PMID: 36816029 PMCID: PMC9936823 DOI: 10.3389/fgene.2023.1089490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
Introduction: Phenotype predictions of beef eating quality for individual animals could be used to allocate animals to longer and more expensive feeding regimes as they enter the feedlot if they are predicted to have higher eating quality, and to sort carcasses into consumer or market value categories. Phenotype predictions can include genetic effects (breed effects, heterosis and breeding value), predicted from genetic markers, as well as fixed effects such as days aged and carcass weight, hump height, ossification, and hormone growth promotant (HGP) status. Methods: Here we assessed accuracy of phenotype predictions for five eating quality traits (tenderness, juiciness, flavour, overall liking and MQ4) in striploins from 1701 animals from a wide variety of backgrounds, including Bos indicus and Bos taurus breeds, using genotypes and simple fixed effects including days aged and carcass weight. The genetic components were predicted based on 709k single nucleotide polymorphism (SNP) using BayesR model, which assumes some markers may have a moderate to large effect. Fixed effects in the prediction included principal components of the genomic relationship matrix, to account for breed effects, heterosis, days aged and carcass weight. Results and Discussion: A model which allowed breed effects to be captured in the SNP effects (e.g., not explicitly fitting these effects) tended to have slightly higher accuracies (0.43-0.50) compared to when these effects were explicitly fitted as fixed effects (0.42-0.49), perhaps because breed effects when explicitly fitted were estimated with more error than when incorporated into the (random) SNP effects. Adding estimates of effects of days aged and carcass weight did not increase the accuracy of phenotype predictions in this particular analysis. The accuracy of phenotype prediction for beef eating quality traits was sufficiently high that such predictions could be useful in predicting eating quality from DNA samples taken from an animal/carcass as it enters the processing plant, to enable optimal supply chain value extraction by sorting product into markets with different quality. The BayesR predictions identified several novel genes potentially associated with beef eating quality.
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Affiliation(s)
- Mehrnush Forutan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia,*Correspondence: Mehrnush Forutan,
| | - Andrew Lynn
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia
| | - Hassan Aliloo
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia
| | - Samuel A. Clark
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia
| | - Peter McGilchrist
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia
| | | | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
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A Comparative Study of Machine Learning Methods for Predicting Live Weight of Duroc, Landrace, and Yorkshire Pigs. Animals (Basel) 2022; 12:ani12091152. [PMID: 35565577 PMCID: PMC9104573 DOI: 10.3390/ani12091152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/20/2022] [Accepted: 04/28/2022] [Indexed: 11/25/2022] Open
Abstract
Simple Summary Live weight is an important indicator of livestock productivity and serves as an informative measure for the health, feeding, breeding, and selection of livestock. In this paper, the live weight of pig was estimated using six morphometric measurements, breed, weight at birth, weight at weaning, and age at weaning. In the present paper, we propose a comparative analysis of various machine learning methods using outlier detection, normalisation, hyperparameter optimisation, and stack generalisation to increase the accuracy of the predictions of the live weight of pigs. The StackingRegressor algorithm yielded a prediction quality of the live weight of Duroc, Landrace, and Yorkshire pigs that was higher than that of the state-of-the art algorithms. Abstract Live weight is an important indicator of livestock productivity and serves as an informative measure for the health, feeding, breeding, and selection of livestock. In this paper, the live weight of pig was estimated using six morphometric measurements, weight at birth, weight at weaning, and age at weaning. This study utilised a dataset including 340 pigs of the Duroc, Landrace, and Yorkshire breeds. In the present paper, we propose a comparative analysis of various machine learning methods using outlier detection, normalisation, hyperparameter optimisation, and stack generalisation to increase the accuracy of the predictions of the live weight of pigs. The performance of live weight prediction was assessed based on the evaluation criteria: the coefficient of determination, the root-mean-squared error, the mean absolute error, and the mean absolute percentage error. The performance measures in our experiments were also validated through 10-fold cross-validation to provide a robust model for predicting the pig live weight. The StackingRegressor model was found to provide the best results with an MAE of 4.331 and a MAPE of 4.296 on the test dataset.
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Yoosefzadeh-Najafabadi M, Tulpan D, Eskandari M. Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. PLoS One 2021; 16:e0250665. [PMID: 33930039 PMCID: PMC8087002 DOI: 10.1371/journal.pone.0250665] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 04/12/2021] [Indexed: 12/02/2022] Open
Abstract
Improving genetic yield potential in major food grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soybean were measured using a panel of 250 genotypes grown in four environments. These traits were the number of nodes per plant (NP), number of non-reproductive nodes per plant (NRNP), number of reproductive nodes per plant (RNP), number of pods per plant (PP), and the ratio of number of pods to number of nodes per plant (P/N). These data were used for predicting the total soybean seed yield using the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Random Forest (RF), machine learning (ML) algorithms, individually and collectively through an ensemble method based on bagging strategy (E-B). The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. Using the E-B algorithm, we were able to increase the prediction accuracy by improving the values of R2, MAE, and RMSE by 0.1, 0.24 kg.ha-1, and 0.96 kg.ha-1, respectively. Furthermore, for the first time in this study, we allied the E-B with the genetic algorithm (GA) to model the optimum values of yield components in an ideotype genotype in which the yield is maximized. The results revealed a better understanding of the relationships between soybean yield and its components, which can be used for selecting parental lines and designing promising crosses for developing cultivars with improved genetic yield potential.
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Affiliation(s)
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada
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Mollenhorst H, Ducro BJ, De Greef KH, Hulsegge I, Kamphuis C. Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1. J Anim Sci 2020; 97:4152-4159. [PMID: 31504579 PMCID: PMC6776275 DOI: 10.1093/jas/skz274] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 08/20/2019] [Indexed: 12/21/2022] Open
Abstract
In pig production, efficiency is benefiting from uniform growth in pens resulting in single deliveries from a pen of possibly all animals in the targeted weight range. Abnormalities, like pneumonia or aberrant growth, reduce production efficiency as it reduces the uniformity and might cause multiple deliveries per batch and pigs delivered with a low meat yield or outside the targeted weight range. Early identification of pigs prone to develop these abnormalities, for example, at the onset of the growing-finishing phase, would help to prevent heterogeneous pens through management interventions. Data about previous production cycles at the farm combined with data from the piglet’s own history may help in identifying these abnormalities. The aim of this study, therefore, was to predict at the onset of the growing-finishing phase, that is, at 3 mo in advance, deviant pigs at slaughter with a machine-learning technique called boosted trees. The dataset used was extracted from the farm management system of a research center. It contained over 70,000 records of individual pigs born between 2004 and 2016, including information on, for example, offspring, litter size, transfer dates between production stages, their respective locations within the barns, and individual live-weights at several production stages. Results obtained on an independent test set showed that at a 90% specificity rate, the sensitivity was 16% for low meat percentage, 20% for pneumonia and 36% for low lifetime growth rate. For low lifetime growth rate, this meant an almost three times increase in positive predictive value compared to the current situation. From these results, it was concluded that routine performance information available at the onset of the growing-finishing phase combined with data about previous production cycles formed a moderate base to identify pigs prone to develop pneumonia (AUC > 0.60) and a good base to identify pigs prone to develop growth aberrations (AUC > 0.70) during the growing-finishing phase. The mentioned information, however, was not a sufficient base to identify pigs prone to develop low meat percentage (AUC < 0.60). The shown ability to identify growth aberrations and pneumonia can be considered a good first step towards the development of an early warning system for pigs in the growing-finishing phase.
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Affiliation(s)
- Herman Mollenhorst
- Wageningen University and Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - Bart J Ducro
- Wageningen University and Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - Karel H De Greef
- Wageningen University and Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - Ina Hulsegge
- Wageningen University and Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - Claudia Kamphuis
- Wageningen University and Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
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Kamphuis C, Duenk P, Veerkamp RF, Visser B, Singh G, Nigsch A, De Mol RM, Broekhuijse MLWJ. Machine learning to further improve the decision which boar ejaculates to process into artificial insemination doses. Theriogenology 2019; 144:112-121. [PMID: 31927416 DOI: 10.1016/j.theriogenology.2019.12.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/31/2019] [Accepted: 12/23/2019] [Indexed: 01/13/2023]
Abstract
Current artificial insemination (AI) laboratory practices assess semen quality of each boar ejaculate to decide which ones to process into AI doses. This decision is aided with two, world-wide used, motility parameters that come available through computer assisted semen analysis (CASA). This decision process, however, still results in AI doses with variable and sometimes suboptimal fertility outcomes (e.g., small litter size). The hypothesis was that the decision which ejaculates to process into AI doses can be improved by adding more data from CASA systems, and data from other sources, in combination with a data-driven model. Available data consisted of ejaculates that passed the initial decision, and thus, were processed into AI doses and used to inseminate sows. Data were divided into a training set (6793 records) and a validation set (1191 records) for model development, and an independent test set (1434 records) for performance assessment. Gradient Boosting Machine (GBM) models were developed to predict four fertility phenotypes of interest (gestation length, total number born, number born alive, and number of stillborn piglets). Each fertility phenotype was considered as a numeric and as a binary outcome parameter, totaling to eight different fertility phenotypes. Data used to further improve the decision process originated from four sources: 1) CASA information, 2) boar ejaculate information, 3) breeding value estimations, and 4) weather information. These data were used to create seven prediction sets, where each new set added parameters to the ones included in the previous set. The GBM models predicted fertility phenotypes with low correlations (for numeric phenotypes) and area under the curve values (for binary phenotypes) on the test data. Hence, results demonstrated that a combination of more data and GBM did not enable further improvement of the AI dose quality checks, resulting in the rejection of our hypothesis. However, our study revealed parameters affecting boar ejaculate fertility which were not used in today's decision process. These parameters (listed in the top 10 in at least four GBM models) included one parameter associated with boar ejaculate information, two with breeding value estimations, five with CASA information, and one with weather information. These parameters, therefore, should be further investigated for their potential value when assessing the quality of boar ejaculates in daily routine AI doses processing.
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Affiliation(s)
- Claudia Kamphuis
- Wageningen University & Research, Animal Breeding and Genomics, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
| | - Pascal Duenk
- Wageningen University & Research, Animal Breeding and Genomics, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - Roel Franciscus Veerkamp
- Wageningen University & Research, Animal Breeding and Genomics, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - Bram Visser
- Hendrix Genetics Research, Technology & Services B.V., Spoorstraat 69, 5831 CK, Boxmeer, the Netherlands
| | - Gurnoor Singh
- Radboud University Medical Center, The Centre for Molecular and Biomolecular Informatics, Nijmegen, the Netherlands
| | - Annette Nigsch
- Wageningen University & Research, Department of Quantitative Veterinary Epidemiology, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - Rudi Maria De Mol
- Wageningen University & Research, Animal Welfare & Adaptation, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
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