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Lau CF, Malek S, Gunalan R, Saw A, Milow P, Song C. Paediatric orthopaedic expert system. Health Informatics J 2023; 29:14604582231218530. [PMID: 38019888 DOI: 10.1177/14604582231218530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
The paediatric orthopaedic expert system analyses and predicts the healing time of limb fractures in children using machine learning. As far we know, no published research on the paediatric orthopaedic expert system that predicts paediatric fracture healing time using machine learning has been published. The University Malaya Medical Centre (UMMC) offers paediatric orthopaedic data, comprises children under the age of 12 radiographs limb fractures with ages recorded from the date and time of initial trauma. SVR algorithms are used to predict and discover variables associated with fracture healing time. This study developed an expert system capable of predicting healing time, which can assist general practitioners and healthcare practitioners during treatment and follow-up. The system is available online at https://kidsfractureexpert.com/.
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Affiliation(s)
- Chia Fong Lau
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Sorayya Malek
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Roshan Gunalan
- Department of Orthopaedics / NOCERAL, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Aik Saw
- Department of Orthopaedics / NOCERAL, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Pozi Milow
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Cheen Song
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
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Armero C, Cabras S, Castellanos ME, Quirós A. Two-Stage Bayesian Approach for GWAS With Known Genealogy. J Comput Graph Stat 2018. [DOI: 10.1080/10618600.2018.1483828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Carmen Armero
- Department of Statistics and Operations Research, Universitat de València, València, Spain
| | - Stefano Cabras
- Department of Statistics, Universidad Carlos III de Madrid, Madrid, Spain
- Department of Mathematics and Informatics, Università degli Studi di Cagliari, Cagliari CA, Italy
| | - María Eugenia Castellanos
- Department of Informatics and Statistics, Universidad Rey Juan Carlos, Madrid, Spain
- Department of Economic Science, Università degli Studi di Cagliari, Cagliari CA, Italy
| | - Alicia Quirós
- Department of Mathematics, Universidad de León, León, Spain
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Malek S, Gunalan R, Kedija S, Lau C, Mosleh MA, Milow P, Lee S, Saw A. Random forest and Self Organizing Maps application for analysis of pediatric fracture healing time of the lower limb. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.05.094] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Waldmann P. Genome-wide prediction using Bayesian additive regression trees. Genet Sel Evol 2016; 48:42. [PMID: 27286957 PMCID: PMC4901500 DOI: 10.1186/s12711-016-0219-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 05/26/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The goal of genome-wide prediction (GWP) is to predict phenotypes based on marker genotypes, often obtained through single nucleotide polymorphism (SNP) chips. The major problem with GWP is high-dimensional data from many thousands of SNPs scored on several thousands of individuals. A large number of methods have been developed for GWP, which are mostly parametric methods that assume statistical linearity and only additive genetic effects. The Bayesian additive regression trees (BART) method was recently proposed and is based on the sum of nonparametric regression trees with the priors being used to regularize the parameters. Each regression tree is based on a recursive binary partitioning of the predictor space that approximates an unknown function, which will automatically model nonlinearities within SNPs (dominance) and interactions between SNPs (epistasis). In this study, we introduced BART and compared its predictive performance with that of the LASSO, Bayesian LASSO (BLASSO), genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space (RKHS) regression and random forest (RF) methods. RESULTS Tests on the QTLMAS2010 simulated data, which are mainly based on additive genetic effects, show that cross-validated optimization of BART provides a smaller prediction error than the RF, BLASSO, GBLUP and RKHS methods, and is almost as accurate as the LASSO method. If dominance and epistasis effects are added to the QTLMAS2010 data, the accuracy of BART relative to the other methods was increased. We also showed that BART can produce importance measures on the SNPs through variable inclusion proportions. In evaluations using real data on pigs, the prediction error was smaller with BART than with the other methods. CONCLUSIONS BART was shown to be an accurate method for GWP, in which the regression trees guarantee a very sparse representation of additive and complex non-additive genetic effects. Moreover, the Markov chain Monte Carlo algorithm with Bayesian back-fitting provides a computationally efficient procedure that is suitable for high-dimensional genomic data.
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Affiliation(s)
- Patrik Waldmann
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences (SLU), Box 7023, 750 07, Uppsala, Sweden.
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Lu P, Abedi V, Mei Y, Hontecillas R, Hoops S, Carbo A, Bassaganya-Riera J. Supervised learning methods in modeling of CD4+ T cell heterogeneity. BioData Min 2015; 8:27. [PMID: 26339293 PMCID: PMC4559362 DOI: 10.1186/s13040-015-0060-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Accepted: 08/25/2015] [Indexed: 01/11/2023] Open
Abstract
Background Modeling of the immune system – a highly non-linear and complex system – requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales. Methods This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models. Results Our results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF. Conclusions Using machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities.
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Affiliation(s)
- Pinyi Lu
- The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA ; Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA
| | - Vida Abedi
- The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA ; Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA
| | - Yongguo Mei
- The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA ; Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA
| | - Raquel Hontecillas
- The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA ; Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA
| | - Stefan Hoops
- The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA ; Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA
| | - Adria Carbo
- BioTherapeutics Inc, 1800 Kraft Drive, Suite 200, Blacksburg, VA 24060 USA
| | - Josep Bassaganya-Riera
- The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA ; Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA
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