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Liang YR, Zhou JP, Luo YX, Su QB, Ye KW, Lu FY, Wu PS. Factors influencing recurrence and model development for recurrence of minimally invasive percutaneous transhepatic lithotripsy: a single-center retrospective study. Am J Transl Res 2024; 16:1740-1748. [PMID: 38883341 PMCID: PMC11170565 DOI: 10.62347/tvry9827] [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: 03/06/2024] [Accepted: 04/28/2024] [Indexed: 06/18/2024]
Abstract
OBJECTIVE To identify factors influencing recurrence after percutaneous transhepatic choledochoscopic lithotripsy (PTCSL) and to develop a predictive model. METHODS We retrospectively analyzed clinical data from 354 patients with intrahepatic and extrahepatic bile duct stones treated with PTCSL at Qinzhou First People's Hospital between February 2018 and January 2020. Patients were followed for three years and categorized into non-recurrence and recurrence groups based on postoperative outcome. Univariate analysis identified possible predictors of stone recurrence. Data were split using the gradient boosting machine (GBM) algorithm, assigning 70% as the training set and 30% as the test set. The predictive performance of the GBM model was assessed using the receiver operating characteristic (ROC) curve and calibration curve, and compared with a logistic regression model. RESULTS Six factors were identified as significant predictors of recurrence: age, diabetes, total bilirubin, biliary stricture, number of stones, and stone diameter. The GBM model, developed based on these factors, showed high predictive accuracy. The area under the ROC curve (AUC) was 0.763 (95% CI: 0.695-0.830) for the training set and 0.709 (95% CI: 0.596-0.822) for the test set. Optimal cutoff values were 0.286 and 0.264, with sensitivities of 62.30% and 66.70%, and specificities of 77.20% and 68.50%, respectively. Calibration curves indicated good agreement between predicted probabilities and observed recurrence rates in both sets. DeLong's test revealed no significant differences between the GBM and logistic regression models in predictive performance (training set: D = 0.003, P = 0.997 > 0.05; test set: D = 0.075, P = 0.940 > 0.05). CONCLUSION Biliary stricture, stone diameter, diabetes, stone number, age, and total bilirubin significantly influence stone recurrence after PTCSL. The GBM model, based on these factors, demonstrates robust accuracy and discrimination. Both GBM and logistic regression models effectively predicted stone recurrence post-PTCSL.
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Affiliation(s)
- Yong-Rong Liang
- Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China
| | - Jia-Peng Zhou
- Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China
| | - Yong-Xiang Luo
- Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China
| | - Qi-Bin Su
- Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China
| | - Kun-Wei Ye
- Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China
| | - Fu-Yan Lu
- Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China
| | - Pei-Sheng Wu
- Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China
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Fernández-González J, Haquin B, Combes E, Bernard K, Allard A, Isidro Y Sánchez J. Maximizing efficiency in sunflower breeding through historical data optimization. PLANT METHODS 2024; 20:42. [PMID: 38493115 PMCID: PMC10943787 DOI: 10.1186/s13007-024-01151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/30/2024] [Indexed: 03/18/2024]
Abstract
Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.
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Affiliation(s)
- Javier Fernández-González
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
| | | | | | | | | | - Julio Isidro Y Sánchez
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
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Zou J, Shen YK, Wu SN, Wei H, Li QJ, Xu SH, Ling Q, Kang M, Liu ZL, Huang H, Chen X, Wang YX, Liao XL, Tan G, Shao Y. Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study. Technol Cancer Res Treat 2024; 23:15330338231219352. [PMID: 38233736 PMCID: PMC10865948 DOI: 10.1177/15330338231219352] [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: 11/30/2022] [Revised: 10/10/2023] [Accepted: 11/08/2023] [Indexed: 01/19/2024] Open
Abstract
Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.
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Affiliation(s)
- Jie Zou
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
| | - Yan-Kun Shen
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
| | - Shi-Nan Wu
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, People's Republic of China
| | - Hong Wei
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
| | - Qing-Jian Li
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, People's Republic of China
| | - San Hua Xu
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
| | - Qian Ling
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
| | - Min Kang
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
| | - Zhao-Lin Liu
- Department of Ophthalmology, the First Affiliated Hospital of University of South China, Hunan Branch of National Clinical Research Center for Ocular Disease, Hengyan, Hunan Province, People's Republic of China
| | - Hui Huang
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
| | - Xu Chen
- Department of Ophthalmology and Visual Sciences, Maastricht University, Maastricht, Limburg Province, Netherlands
| | - Yi-Xin Wang
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
| | - Xu-Lin Liao
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, People's Republic of China
| | - Gang Tan
- Department of Ophthalmology, the First Affiliated Hospital of University of South China, Hunan Branch of National Clinical Research Center for Ocular Disease, Hengyan, Hunan Province, People's Republic of China
| | - Yi Shao
- Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Branch of National Clinical Research Center for Ocular Disease, Nanchang, Jiangxi, People's Republic of China
- Current affiliation: Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China
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Weber SE, Chawla HS, Ehrig L, Hickey LT, Frisch M, Snowdon RJ. Accurate prediction of quantitative traits with failed SNP calls in canola and maize. FRONTIERS IN PLANT SCIENCE 2023; 14:1221750. [PMID: 37936929 PMCID: PMC10627008 DOI: 10.3389/fpls.2023.1221750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/05/2023] [Indexed: 11/09/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | | | - Lennard Ehrig
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Lee T. Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
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Aldisi R, Hassanin E, Sivalingam S, Buness A, Klinkhammer H, Mayr A, Fröhlich H, Krawitz P, Maj C. Gene-based burden scores identify rare variant associations for 28 blood biomarkers. BMC Genom Data 2023; 24:50. [PMID: 37667186 PMCID: PMC10476296 DOI: 10.1186/s12863-023-01155-0] [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: 11/14/2022] [Accepted: 08/28/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling. METHODS We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined). RESULTS Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models. CONCLUSION This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level.
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Affiliation(s)
- Rana Aldisi
- Institute of Genomic Statistic and Bioinformatics, University Hospital Bonn, Bonn, Germany.
| | - Emadeldin Hassanin
- Institute of Genomic Statistic and Bioinformatics, University Hospital Bonn, Bonn, Germany
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Sugirthan Sivalingam
- Institute of Genomic Statistic and Bioinformatics, University Hospital Bonn, Bonn, Germany
- Core Unit for Bioinformatics Analysis, University Hospital Bonn, Bonn, Germany
- Institute of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Andreas Buness
- Institute of Genomic Statistic and Bioinformatics, University Hospital Bonn, Bonn, Germany
- Core Unit for Bioinformatics Analysis, University Hospital Bonn, Bonn, Germany
- Institute of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Hannah Klinkhammer
- Institute of Genomic Statistic and Bioinformatics, University Hospital Bonn, Bonn, Germany
- Institute of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Andreas Mayr
- Institute of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany
| | - Peter Krawitz
- Institute of Genomic Statistic and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Carlo Maj
- Institute of Genomic Statistic and Bioinformatics, University Hospital Bonn, Bonn, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
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Perez BC, Bink MCAM, Svenson KL, Churchill GA, Calus MPL. Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence. G3 (BETHESDA, MD.) 2022; 12:jkac258. [PMID: 36161485 PMCID: PMC9635642 DOI: 10.1093/g3journal/jkac258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.
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Affiliation(s)
- Bruno C Perez
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | - Marco C A M Bink
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | | | | | - Mario P L Calus
- Corresponding author: Animal Breeding and Genomics, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands.
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Montesinos-López OA, Gonzalez HN, Montesinos-López A, Daza-Torres M, Lillemo M, Montesinos-López JC, Crossa J. Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding. THE PLANT GENOME 2022; 15:e20214. [PMID: 35535459 DOI: 10.1002/tpg2.20214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is a predictive methodology that is changing plant breeding. Genomic selection trains a statistical machine-learning model using available phenotypic and genotypic data with which predictions are performed for individuals that were only genotyped. For this reason, some statistical machine-learning methods are being implemented in GS, but in order to improve the selection of new genotypes early in the prediction process, the exploration of new statistical machine-learning algorithms must continue. In this paper, we performed a benchmarking study between the Bayesian threshold genomic best linear unbiased predictor model (TGBLUP; popular in GS) and the gradient boosting machine (GBM). This comparison was done using four real wheat (Triticum aestivum L.) data sets with categorical traits measured in terms of two metrics: the proportion of cases correctly classified (PCCC) and the Kappa coefficient in the testing set. Under 10 random partitions with four different sizes of testing proportions (20, 40, 60, and 80%), we compared the two algorithms and found that in three of the four data sets, the GBM outperformed the TGBLUP model in terms of both metrics (PCCC and Kappa coefficient). In the larger data sets (Data Sets 3 and 4), the gain in terms of prediction accuracy of the GBM was considerably significant. For this reason, we encourage more research using the GBM in GS to evaluate its virtues in terms of prediction performance in the context of GS.
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Affiliation(s)
| | | | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Univ. de Guadalajara, Guadalajara, Jalisco, 44430, México
| | - María Daza-Torres
- Dep. of Public Health Sciences, Univ. of California, Davis, CA, 95616, USA
| | - Morten Lillemo
- Dep. of Plant Sciences, Norwegian Univ. of Life Sciences, IHA/CIGENE, P.O. Box 5003, NO-1432, Ås, Norway
| | | | - José Crossa
- Colegio de Postgraduados, Montecillos, Edo. de México, 56230, México
- Biometrics and Statistics Unit, Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera, México-Veracruz, 52640, México
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