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Huang Y, Wipat A, Bacardit J. Transcriptional biomarker discovery toward building a load stress reporting system for engineered Escherichia coli strains. Biotechnol Bioeng 2024; 121:355-365. [PMID: 37807718 PMCID: PMC10953381 DOI: 10.1002/bit.28567] [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: 05/09/2023] [Revised: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 10/10/2023]
Abstract
Foreign proteins are produced by introducing synthetic constructs into host bacteria for biotechnology applications. This process can cause resource competition between synthetic circuits and host cells, placing a metabolic burden on the host cells which may result in load stress and detrimental physiological changes. Consequently, the host bacteria can experience slow growth, and the synthetic system may suffer from suboptimal function. To help in the detection of bacterial load stress, we developed machine-learning strategies to select a minimal number of genes that could serve as biomarkers for the design of load stress reporters. We identified pairs of biomarkers that showed discriminative capacity to detect the load stress states induced in 41 engineered Escherichia coli strains.
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Affiliation(s)
- Yiming Huang
- Interdisciplinary Computing and Complex BioSystems GroupNewcastle UniversityNewcastle upon TyneUK
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems GroupNewcastle UniversityNewcastle upon TyneUK
| | - Jaume Bacardit
- Interdisciplinary Computing and Complex BioSystems GroupNewcastle UniversityNewcastle upon TyneUK
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2
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Huang Y, Sinha N, Wipat A, Bacardit J. A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis. Synth Syst Biotechnol 2022; 8:97-106. [PMID: 36605706 PMCID: PMC9794971 DOI: 10.1016/j.synbio.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 11/29/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022] Open
Abstract
One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts, as these stress states can lead to suboptimal growth and lower yields of target products. To enable the design of genetic circuits for reporting or mitigating the stress states, it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria. Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria, motivating computational methods that can identify robust biomarkers for experimental characterisation and verification. Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis, this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments. We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification (mean f1-score achieved at 0.99), as well as in a range of independent datasets (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.
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Affiliation(s)
- Yiming Huang
- Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, UK,Corresponding authors.
| | - Nishant Sinha
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, UK
| | - Jaume Bacardit
- Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, UK,Corresponding authors.
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3
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Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochem Soc Trans 2022; 50:241-252. [PMID: 35076690 PMCID: PMC9022974 DOI: 10.1042/bst20211240] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/23/2021] [Accepted: 12/23/2021] [Indexed: 12/18/2022]
Abstract
There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.
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Identification of CNGB1 as a Predictor of Response to Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer. Cancers (Basel) 2021; 13:cancers13153903. [PMID: 34359804 PMCID: PMC8345622 DOI: 10.3390/cancers13153903] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 01/12/2023] Open
Abstract
Simple Summary Chemotherapy is recommended prior to surgical removal of the bladder for muscle-invasive bladder cancer patients. Despite a survival benefit, some patients do not respond and experience substantial toxicity and delay in surgery. Therefore, the identification of chemotherapy responders before initiating therapy would be a helpful clinical asset. To date, there are no reliable biomarkers routinely used in clinical practice that identify patients most likely to benefit from chemotherapy and their identification is urgently required for more precise delivery of care. To address this issue, we compared gene expression profiles of biopsy materials from 30 chemotherapy-responder and -non-responder patients. This analysis revealed a novel signature gene set and CNGB1 as a simpler proxy as a promising biomarker to predict chemoresponsiveness of muscle-invasive bladder cancer patients. Our findings require further validation in larger patient cohorts and in a clinical trial setting. Abstract Cisplatin-based neoadjuvant chemotherapy (NAC) is recommended prior to radical cystectomy for muscle-invasive bladder cancer (MIBC) patients. Despite a 5–10% survival benefit, some patients do not respond and experience substantial toxicity and delay in surgery. To date, there are no clinically approved biomarkers predictive of response to NAC and their identification is urgently required for more precise delivery of care. To address this issue, a multi-methods analysis approach of machine learning and differential gene expression analysis was undertaken on a cohort of 30 MIBC cases highly selected for an exquisitely strong response to NAC or marked resistance and/or progression (discovery cohort). RGIFE (ranked guided iterative feature elimination) machine learning algorithm, previously demonstrated to have the ability to select biomarkers with high predictive power, identified a 9-gene signature (CNGB1, GGH, HIST1H4F, IDO1, KIF5A, MRPL4, NCDN, PRRT3, SLC35B3) able to select responders from non-responders with 100% predictive accuracy. This novel signature correlated with overall survival in meta-analysis performed using published NAC treated-MIBC microarray data (validation cohort 1, n = 26, Log rank test, p = 0.02). Corroboration with differential gene expression analysis revealed cyclic nucleotide-gated channel, CNGB1, as the top ranked upregulated gene in non-responders to NAC. A higher CNGB1 immunostaining score was seen in non-responders in tissue microarray analysis of the discovery cohort (n = 30, p = 0.02). Kaplan-Meier analysis of a further cohort of MIBC patients (validation cohort 2, n = 99) demonstrated that a high level of CNGB1 expression associated with shorter cancer specific survival (p < 0.001). Finally, in vitro studies showed siRNA-mediated CNGB1 knockdown enhanced cisplatin sensitivity of MIBC cell lines, J82 and 253JB-V. Overall, these data reveal a novel signature gene set and CNGB1 as a simpler proxy as a promising biomarker to predict chemoresponsiveness of MIBC patients.
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Huang Y, Smith W, Harwood C, Wipat A, Bacardit J. Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis. SENSORS (BASEL, SWITZERLAND) 2021; 21:2436. [PMID: 33916259 PMCID: PMC8036383 DOI: 10.3390/s21072436] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 01/08/2023]
Abstract
A goal of the biotechnology industry is to be able to recognise detrimental cellular states that may lead to suboptimal or anomalous growth in a bacterial population. Our current knowledge of how different environmental treatments modulate gene regulation and bring about physiology adaptations is limited, and hence it is difficult to determine the mechanisms that lead to their effects. Patterns of gene expression, revealed using technologies such as microarrays or RNA-seq, can provide useful biomarkers of different gene regulatory states indicative of a bacterium's physiological status. It is desirable to have only a few key genes as the biomarkers to reduce the costs of determining the transcriptional state by opening the way for methods such as quantitative RT-PCR and amplicon panels. In this paper, we used unsupervised machine learning to construct a transcriptional landscape model from condition-dependent transcriptome data, from which we have identified 10 clusters of samples with differentiated gene expression profiles and linked to different cellular growth states. Using an iterative feature elimination strategy, we identified a minimal panel of 10 biomarker genes that achieved 100% cross-validation accuracy in predicting the cluster assignment. Moreover, we designed and evaluated a variety of data processing strategies to ensure our methods were able to generate meaningful transcriptional landscape models, capturing relevant biological processes. Overall, the computational strategies introduced in this study facilitate the identification of a detailed set of relevant cellular growth states, and how to sense them using a reduced biomarker panel.
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Affiliation(s)
- Yiming Huang
- Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (Y.H.); (W.S.)
| | - Wendy Smith
- Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (Y.H.); (W.S.)
| | - Colin Harwood
- Centre for Bacterial Cell Biology, Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (Y.H.); (W.S.)
| | - Jaume Bacardit
- Interdisciplinary Computing and Complex BioSystems (ICOS) Group, School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (Y.H.); (W.S.)
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Yousef M, Ülgen E, Uğur Sezerman O. CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis. PeerJ Comput Sci 2021; 7:e336. [PMID: 33816987 PMCID: PMC7959595 DOI: 10.7717/peerj-cs.336] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 11/23/2020] [Indexed: 05/04/2023]
Abstract
Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological resources. In this study, we propose a new computational approach named CogNet that is an integrative gene selection tool that exploits biological knowledge for grouping the genes for the computational modeling tasks of ranking and classification. In CogNet, the pathfindR serves as the biological grouping tool to allow the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis results to build a biologically relevant model. CogNet provides a list of significant KEGG pathways that can classify the data with a very high accuracy. The list also provides the genes belonging to these pathways that are differentially expressed that are used as features in the classification problem. The list facilitates deep analysis and better interpretability of the role of KEGG pathways in classification of the data thus better establishing the biological relevance of these differentially expressed genes. Even though the main aim of our study is not to improve the accuracy of any existing tool, the performance of the CogNet outperforms a similar approach called maTE while obtaining similar performance compared to other similar tools including SVM-RCE. CogNet was tested on 13 gene expression datasets concerning a variety of diseases.
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Affiliation(s)
- Malik Yousef
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel
- Department of Information Systems, Zefat Academic College, Zefat, Israel
| | - Ege Ülgen
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Osman Uğur Sezerman
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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Yousef M, Kumar A, Bakir-Gungor B. Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data. ENTROPY (BASEL, SWITZERLAND) 2020; 23:E2. [PMID: 33374969 PMCID: PMC7821996 DOI: 10.3390/e23010002] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 12/19/2022]
Abstract
In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. One of the main goals of this review is to explore the existing methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid the prediction, diagnosis, and treatment of diseases, as well as to enlighten us on disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. Another aim of this review is to boost the bioinformatics community to develop new approaches for searching and determining significant groups/clusters of features based on one or more biological grouping functions.
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Affiliation(s)
- Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat 13206, Israel
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat 13206, Israel
| | - Abhishek Kumar
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India;
- Manipal Academy of Higher Education (MAHE), Manipal 576104, India
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri 38080, Turkey;
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Acharjee A, Larkman J, Xu Y, Cardoso VR, Gkoutos GV. A random forest based biomarker discovery and power analysis framework for diagnostics research. BMC Med Genomics 2020; 13:178. [PMID: 33228632 PMCID: PMC7685541 DOI: 10.1186/s12920-020-00826-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 11/15/2020] [Indexed: 11/25/2022] Open
Abstract
Background Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale –omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge. Methods In this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies. Results We present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface (https://joelarkman.shinyapps.io/PowerTools/) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context. Conclusions We developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study.
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Affiliation(s)
- Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK. .,Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, Birmingham, B15 2TT, UK. .,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB, UK.
| | - Joseph Larkman
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, Birmingham, B15 2TT, UK
| | - Yuanwei Xu
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, Birmingham, B15 2TT, UK
| | - Victor Roth Cardoso
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, Birmingham, B15 2TT, UK.,MRC Health Data Research UK (HDR UK), London, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, Birmingham, B15 2TT, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB, UK.,MRC Health Data Research UK (HDR UK), London, UK.,NIHR Experimental Cancer Medicine Centre, Birmingham, B15 2TT, UK.,NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham, B15 2TT, UK
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Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods. Med Biol Eng Comput 2018; 57:159-176. [DOI: 10.1007/s11517-018-1874-4] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 07/12/2018] [Indexed: 12/25/2022]
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10
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Lazzarini N, Runhaar J, Bay-Jensen AC, Thudium CS, Bierma-Zeinstra SMA, Henrotin Y, Bacardit J. A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. Osteoarthritis Cartilage 2017; 25:2014-2021. [PMID: 28899843 DOI: 10.1016/j.joca.2017.09.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 08/16/2017] [Accepted: 09/02/2017] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low sensitivity. Therefore, there is a need for new biomarkers to correctly identify early knee OA. METHOD We have created an analytics pipeline based on machine learning to identify small models (having few variables) that predict the 30-months incidence of knee OA (using multiple clinical and structural OA outcome measures) in overweight middle-aged women without knee OA at baseline. The data included clinical variables, food and pain questionnaires, biochemical markers (BM) and imaging-based information. RESULTS All the models showed high performance (AUC > 0.7) while using only a few variables. We identified both the importance of each variable within the models as well its direction. Finally, we compared the performance of two models with the state-of-the-art approaches available in the literature. CONCLUSIONS We showed the potential of applying machine learning to generate predictive models for the knee OA incidence. Imaging-based information were found particularly important in the proposed models. Furthermore, our analysis confirmed the relevance of known BM for knee OA. Overall, we propose five highly predictive small models that can be possibly adopted for an early prediction of knee OA.
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Affiliation(s)
- N Lazzarini
- ICOS Research Group, School of Computing, Newcastle University, UK; D-BOARD Consortium, An FP7 Programme By the European Committee
| | - J Runhaar
- D-BOARD Consortium, An FP7 Programme By the European Committee; Erasmus University Medical Center Rotterdam, the Netherlands, Dept. of General Practice
| | - A C Bay-Jensen
- D-BOARD Consortium, An FP7 Programme By the European Committee; Nordic Bioscience, Copenhagen, Denmark
| | - C S Thudium
- D-BOARD Consortium, An FP7 Programme By the European Committee; Nordic Bioscience, Copenhagen, Denmark
| | - S M A Bierma-Zeinstra
- D-BOARD Consortium, An FP7 Programme By the European Committee; Erasmus University Medical Center Rotterdam, the Netherlands, Dept. of General Practice; Erasmus University Medical Center Rotterdam, the Netherlands, Dept. of Orthopedics
| | - Y Henrotin
- D-BOARD Consortium, An FP7 Programme By the European Committee; University of Liège, Belgium; Artialis SA, Liège, Belgium
| | - J Bacardit
- ICOS Research Group, School of Computing, Newcastle University, UK; D-BOARD Consortium, An FP7 Programme By the European Committee.
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