{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T08:16:26Z","timestamp":1779264986066,"version":"3.51.4"},"reference-count":105,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Abdullah II Fund for Development","award":["5\/2017"],"award-info":[{"award-number":["5\/2017"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of <jats:italic>CD36<\/jats:italic> gene rs1761667 (G&gt;A) and rs1527483 (C&gt;T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (<jats:italic>p<\/jats:italic> &gt; 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (\u2265 0.75) and Cohen\u2019s kappa (\u03ba) (\u2265 0.5). Interestingly, in K-star tool, the accuracy and Cohen\u2019s \u03ba values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between <jats:italic>CD36<\/jats:italic> polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.<\/jats:p>","DOI":"10.1371\/journal.pone.0257857","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T18:18:35Z","timestamp":1634235515000},"page":"e0257857","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1745-8985","authenticated-orcid":true,"given":"Ma\u2019mon M.","family":"Hatmal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2946-7328","authenticated-orcid":true,"given":"Walhan","family":"Alshaer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6210-9832","authenticated-orcid":true,"given":"Ismail S.","family":"Mahmoud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8249-9519","authenticated-orcid":true,"given":"Mohammad A. I.","family":"Al-Hatamleh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1681-6747","authenticated-orcid":true,"given":"Hamzeh J.","family":"Al-Ameer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Abuyaman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malek","family":"Zihlif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohimah","family":"Mohamud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mais","family":"Darras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Al Shhab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rand","family":"Abu-Raideh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hilweh","family":"Ismail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Al-Hamadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Abdelhay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"issue":"3","key":"pone.0257857.ref001","first-page":"74","article-title":"Current progress of human trials using stem cell therapy as a treatment for diabetes mellitus","volume":"5","author":"SK Cheng","year":"2016","journal-title":"Am J Stem Cells"},{"issue":"2","key":"pone.0257857.ref002","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.diabres.2013.11.002","article-title":"Global estimates of diabetes prevalence for 2013 and projections for 2035","volume":"103","author":"L Guariguata","year":"2014","journal-title":"Diabetes Res Clin Pract"},{"issue":"Suppl 2","key":"pone.0257857.ref003","doi-asserted-by":"crossref","first-page":"S120","DOI":"10.2337\/dcS13-2009","article-title":"Genetic screening for the risk of type 2 diabetes: worthless or valuable?","volume":"36","author":"V Lyssenko","year":"2013","journal-title":"Diabetes Care"},{"key":"pone.0257857.ref004","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.2147\/DDDT.S133453","article-title":"Past and current perspective on new therapeutic targets for Type-II diabetes","volume":"11","author":"PD Patil","year":"2017","journal-title":"Drug Des Devel Ther"},{"issue":"8","key":"pone.0257857.ref005","doi-asserted-by":"crossref","first-page":"222","DOI":"10.3390\/biology9080222","article-title":"Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data","volume":"9","author":"MmM Hatmal","year":"2020","journal-title":"Biology"},{"issue":"3","key":"pone.0257857.ref006","doi-asserted-by":"crossref","first-page":"181","DOI":"10.4239\/wjd.v10.i3.181","article-title":"Screening the RFX6-DNA binding domain for potential genetic variants in patients with type 2 diabetes","volume":"10","author":"IS Mahmoud","year":"2019","journal-title":"World J Diabetes"},{"key":"pone.0257857.ref007","first-page":"e14036","article-title":"No impact of soluble epoxide hydrolase rs4149243, rs2234914 and rs751142 genetic variants on the development of type II diabetes and its hypertensive complication among Jordanian patients","author":"M Khamees","year":"2021","journal-title":"Int J Clin Pract"},{"issue":"72","key":"pone.0257857.ref008","doi-asserted-by":"crossref","first-page":"re3","DOI":"10.1126\/scisignal.272re3","article-title":"CD36, a scavenger receptor involved in immunity, metabolism, angiogenesis, and behavior","volume":"2","author":"RL Silverstein","year":"2009","journal-title":"Sci Signal"},{"issue":"2","key":"pone.0257857.ref009","doi-asserted-by":"crossref","first-page":"e59","DOI":"10.1038\/nutd.2013.1","article-title":"The origin of circulating CD36 in type 2 diabetes","volume":"3","author":"MJ Alkhatatbeh","year":"2013","journal-title":"Nutrition & Diabetes"},{"issue":"19","key":"pone.0257857.ref010","doi-asserted-by":"crossref","first-page":"13108","DOI":"10.1074\/jbc.M708086200","article-title":"CD36 mediates both cellular uptake of very long chain fatty acids and their intestinal absorption in mice","volume":"283","author":"VA Drover","year":"2008","journal-title":"J Biol Chem"},{"issue":"42","key":"pone.0257857.ref011","doi-asserted-by":"crossref","first-page":"32523","DOI":"10.1074\/jbc.M003826200","article-title":"Defective uptake and utilization of long chain fatty acids in muscle and adipose tissues of CD36 knockout mice","volume":"275","author":"CT Coburn","year":"2000","journal-title":"J Biol Chem"},{"issue":"4","key":"pone.0257857.ref012","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.ymgme.2010.12.012","article-title":"The macrophage Ox-LDL receptor, CD36 and its association with type II diabetes mellitus","volume":"102","author":"S Gautam","year":"2011","journal-title":"Mol Genet Metab"},{"key":"pone.0257857.ref013","doi-asserted-by":"crossref","first-page":"12837","DOI":"10.1038\/ncomms12837","article-title":"The structural basis for CD36 binding by the malaria parasite","volume":"7","author":"FL Hsieh","year":"2016","journal-title":"Nat Commun"},{"issue":"8","key":"pone.0257857.ref014","doi-asserted-by":"crossref","DOI":"10.3390\/cells9081877","article-title":"The Multifunctionality of CD36 in Diabetes Mellitus and Its Complications-Update in Pathogenesis, Treatment and Monitoring","volume":"9","author":"K Puchalowicz","year":"2020","journal-title":"Cells"},{"issue":"6","key":"pone.0257857.ref015","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1172\/JCI14006","article-title":"CD36: a class B scavenger receptor involved in angiogenesis, atherosclerosis, inflammation, and lipid metabolism","volume":"108","author":"M Febbraio","year":"2001","journal-title":"J Clin Invest"},{"issue":"1","key":"pone.0257857.ref016","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1093\/bmb\/ldy006","article-title":"CD36 and lipid metabolism in the evolution of atherosclerosis","volume":"126","author":"L Zhao","year":"2018","journal-title":"Br Med Bull"},{"key":"pone.0257857.ref017","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.3389\/fphys.2017.01006","article-title":"Polymorphism rs1761667 in the CD36 Gene Is Associated to Changes in Fatty Acid Metabolism and Circulating Endocannabinoid Levels Distinctively in Normal Weight and Obese Subjects","volume":"8","author":"M Melis","year":"2017","journal-title":"Front Physiol"},{"issue":"12","key":"pone.0257857.ref018","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1038\/oby.2009.179","article-title":"Lipid-induced insulin resistance is associated with increased monocyte expression of scavenger receptor CD36 and internalization of oxidized LDL","volume":"17","author":"SR Kashyap","year":"2009","journal-title":"Obesity (Silver Spring)"},{"issue":"5\u20136","key":"pone.0257857.ref019","doi-asserted-by":"crossref","first-page":"288","DOI":"10.2119\/2006-00088.Rac","article-title":"Molecular basis of human CD36 gene mutations","volume":"13","author":"ME Rac","year":"2007","journal-title":"Mol Med"},{"issue":"1","key":"pone.0257857.ref020","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1002\/humu.9256","article-title":"A CD36 nonsense mutation associated with insulin resistance and familial type 2 diabetes","volume":"24","author":"F Lepretre","year":"2004","journal-title":"Hum Mutat"},{"issue":"8","key":"pone.0257857.ref021","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1111\/j.1464-5491.2006.01888.x","article-title":"Direct association of a promoter polymorphism in the CD36\/FAT fatty acid transporter gene with Type 2 diabetes mellitus and insulin resistance","volume":"23","author":"E Corpeleijn","year":"2006","journal-title":"Diabet Med"},{"issue":"11","key":"pone.0257857.ref022","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1093\/hmg\/ddn060","article-title":"Variants in the CD36 gene associate with the metabolic syndrome and high-density lipoprotein cholesterol","volume":"17","author":"L Love-Gregory","year":"2008","journal-title":"Hum Mol Genet"},{"key":"pone.0257857.ref023","first-page":"107","article-title":"Preliminary studies on CD36 gene in type 2 diabetic patients from north India","volume":"134","author":"S Gautam","year":"2011","journal-title":"Indian J Med Res"},{"issue":"3","key":"pone.0257857.ref024","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.ijdm.2010.08.002","article-title":"Association of CD36 gene variants rs1761667 (G>A) and rs1527483 (C>T) with Type 2 diabetes in North Indian population","volume":"2","author":"M Banerjee","year":"2010","journal-title":"International Journal of Diabetes Mellitus"},{"issue":"6","key":"pone.0257857.ref025","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.2337\/diacare.27.6.1496","article-title":"Lipids and Lipoproteins in Patients With Type 2 Diabetes","volume":"27","author":"RM Krauss","year":"2004","journal-title":"Diabetes Care"},{"issue":"1","key":"pone.0257857.ref026","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/1476-511X-9-144","article-title":"Treatment of dyslipidemia in patients with type 2 diabetes","volume":"9","author":"K Vijayaraghavan","year":"2010","journal-title":"Lipids in Health and Disease"},{"key":"pone.0257857.ref027","first-page":"6679512","article-title":"Involvement of Machine Learning Tools in Healthcare Decision Making","volume":"2021","author":"SMDAC Jayatilake","year":"2021","journal-title":"Journal of Healthcare Engineering"},{"key":"pone.0257857.ref028","article-title":"Docking-Generated Multiple Ligand Poses for Bootstrapping Bioactivity Classifying Machine Learning: Repurposing Covalent Inhibitors for COVID-19-Related TMPRSS2 as Case Study","author":"MM Hatmal","year":"2021","journal-title":"Computational and Structural Biotechnology Journal"},{"issue":"4","key":"pone.0257857.ref029","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep Learning in Medical Imaging: General Overview","volume":"18","author":"JG Lee","year":"2017","journal-title":"Korean J Radiol"},{"issue":"6","key":"pone.0257857.ref030","doi-asserted-by":"crossref","first-page":"556","DOI":"10.3390\/vaccines9060556","article-title":"Side Effects and Perceptions Following COVID-19 Vaccination in Jordan: A Randomized, Cross-Sectional Study Implementing Machine Learning for Predicting Severity of Side Effects","volume":"9","author":"MM Hatmal","year":"2021","journal-title":"Vaccines"},{"key":"pone.0257857.ref031","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","article-title":"Machine Learning and Data Mining Methods in Diabetes Research","volume":"15","author":"I Kavakiotis","year":"2017","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"pone.0257857.ref032","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.csbj.2020.12.012","article-title":"Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder","volume":"19","author":"M Wang","year":"2021","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"pone.0257857.ref033","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1016\/j.csbj.2020.05.021","article-title":"Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer","volume":"18","author":"J Gal","year":"2020","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"pone.0257857.ref034","doi-asserted-by":"crossref","first-page":"100976","DOI":"10.1016\/j.molmet.2020.100976","article-title":"Cross-omics analysis revealed gut microbiome-related metabolic pathways underlying atherosclerosis development after antibiotics treatment","volume":"36","author":"BA Kappel","year":"2020","journal-title":"Mol Metab"},{"key":"pone.0257857.ref035","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.molmet.2018.11.003","article-title":"Predicting and understanding the response to short-term intensive insulin therapy in people with early type 2 diabetes","volume":"20","author":"YO Nunez Lopez","year":"2019","journal-title":"Mol Metab"},{"issue":"12","key":"pone.0257857.ref036","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised Spectral\u2013Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification","volume":"12","author":"C Tao","year":"2015","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"8","key":"pone.0257857.ref037","doi-asserted-by":"crossref","first-page":"712","DOI":"10.2174\/1573405614666180322141259","article-title":"Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm","volume":"15","author":"S Maheswari","year":"2019","journal-title":"Curr Med Imaging Rev"},{"key":"pone.0257857.ref038","first-page":"3334","article-title":"Feature extraction and I-NB classification of CT images for early lung cancer detection","volume":"33","author":"B Karthiga","year":"2020","journal-title":"Materials Today: Proceedings"},{"issue":"3","key":"pone.0257857.ref039","doi-asserted-by":"crossref","first-page":"196","DOI":"10.4258\/hir.2016.22.3.196","article-title":"Detection of Cardiovascular Disease Risk\u2019s Level for Adults Using Naive Bayes Classifier","volume":"22","author":"E Miranda","year":"2016","journal-title":"Healthc Inform Res"},{"key":"pone.0257857.ref040","article-title":"Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation","author":"I Wickramasinghe","year":"2020","journal-title":"Soft Computing"},{"key":"pone.0257857.ref041","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1007\/978-3-319-64185-0_28","volume-title":"Digital Forensics and Watermarking","author":"X Ren","year":"2017"},{"key":"pone.0257857.ref042","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/B978-0-12-813314-9.00002-5","volume-title":"Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications","author":"V Rozinajov\u00e1","year":"2018"},{"key":"pone.0257857.ref043","doi-asserted-by":"crossref","first-page":"101076","DOI":"10.1016\/j.molmet.2020.101076","article-title":"Infusion of donor feces affects the gut-brain axis in humans with metabolic syndrome","volume":"42","author":"AV Hartstra","year":"2020","journal-title":"Mol Metab"},{"issue":"5","key":"pone.0257857.ref044","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/s42979-020-00250-8","article-title":"Predictive Supervised Machine Learning Models for Diabetes Mellitus","volume":"1","author":"LJ Muhammad","year":"2020","journal-title":"SN Comput Sci"},{"issue":"10","key":"pone.0257857.ref045","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1016\/j.compbiomed.2013.05.024","article-title":"Automated identification of normal and diabetes heart rate signals using nonlinear measures","volume":"43","author":"U Rajendra Acharya","year":"2013","journal-title":"Comput Biol Med"},{"issue":"9","key":"pone.0257857.ref046","first-page":"389","article-title":"A comparative study on various data mining classification methods: KNN, PNN and ANN for tiles defect detection","volume":"118","author":"C Umamaheswari","year":"2018","journal-title":"International Journal of Pure and Applied Mathematics"},{"issue":"2","key":"pone.0257857.ref047","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S0167-7012(02)00080-5","article-title":"A probabilistic neural network approach for modeling and classification of bacterial growth\/no-growth data","volume":"51","author":"M Hajmeer","year":"2002","journal-title":"Journal of Microbiological Methods"},{"issue":"1","key":"pone.0257857.ref048","doi-asserted-by":"crossref","first-page":"73","DOI":"10.18178\/ijke.2016.2.1.056","article-title":"Diagnosing Breast Cancer Type by Using Probabilistic Neural Network in Decision Support System","volume":"2","author":"A Khosravanian","year":"2016","journal-title":"Int J Knowl Eng"},{"issue":"3","key":"pone.0257857.ref049","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/15730620600961684","article-title":"Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes","volume":"3","author":"DH Tran","year":"2006","journal-title":"Urban Water Journal"},{"key":"pone.0257857.ref050","doi-asserted-by":"crossref","first-page":"68","DOI":"10.4018\/978-1-59904-889-5.ch010","volume-title":"Encyclopedia of Healthcare Information Systems","author":"J Wang","year":"2008"},{"issue":"2","key":"pone.0257857.ref051","first-page":"64","article-title":"Data mining applications in healthcare","volume":"19","author":"HC Koh","year":"2005","journal-title":"J Healthc Inf Manag"},{"key":"pone.0257857.ref052","volume-title":"Advanced methods in neural computing","author":"PD Wasserman","year":"1993"},{"key":"pone.0257857.ref053","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/B978-012646490-0\/50017-2","volume-title":"Soft Computing and Intelligent Systems","author":"P Gupta","year":"2000"},{"key":"pone.0257857.ref054","first-page":"1784","volume-title":"Computer Aided Chemical Engineering","author":"M Sainlez","year":"2011"},{"issue":"12","key":"pone.0257857.ref055","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1007\/s10994-018-5714-4","article-title":"Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation","volume":"107","author":"I Tsamardinos","year":"2018","journal-title":"Machine Learning"},{"issue":"5","key":"pone.0257857.ref056","article-title":"Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy","volume":"10","author":"PK Anand","year":"2020","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"pone.0257857.ref057","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1186\/s12859-018-2090-9","article-title":"Diabetes classification model based on boosting algorithms","volume":"19","author":"P Chen","year":"2018","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"pone.0257857.ref058","first-page":"2249","article-title":"Diabetics Prediction using Gradient Boosted Classifier","volume":"9","author":"B Raja","year":"2019","journal-title":"International Journal of Engineering and Advanced Technology (IJEAT)"},{"issue":"1","key":"pone.0257857.ref059","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1186\/s12911-019-0918-5","article-title":"A data-driven approach to predicting diabetes and cardiovascular disease with machine learning","volume":"19","author":"A Dinh","year":"2019","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"pone.0257857.ref060","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1186\/s12859-019-3027-7","article-title":"An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders","volume":"20","author":"JD Alvarez","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"21","key":"pone.0257857.ref061","doi-asserted-by":"crossref","first-page":"4604","DOI":"10.3390\/app9214604","article-title":"Current Techniques for Diabetes Prediction: Review and Case Study","volume":"9","author":"S Larabi-Marie-Sainte","year":"2019","journal-title":"Applied Sciences"},{"issue":"3","key":"pone.0257857.ref062","doi-asserted-by":"crossref","first-page":"e92549","DOI":"10.1371\/journal.pone.0092549","article-title":"The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort","volume":"9","author":"D Shigemizu","year":"2014","journal-title":"PLoS One"},{"issue":"267","key":"pone.0257857.ref063","article-title":"Machine Learning SNP Based Prediction for Precision Medicine","volume":"10","author":"DSW Ho","year":"2019","journal-title":"Frontiers in Genetics"},{"issue":"3","key":"pone.0257857.ref064","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.tjem.2018.08.001","article-title":"User\u2019s guide to correlation coefficients","volume":"18","author":"H Akoglu","year":"2018","journal-title":"Turk J Emerg Med"},{"key":"pone.0257857.ref065","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"MJ Page","year":"2021","journal-title":"BMJ"},{"key":"pone.0257857.ref066","article-title":"Common variants in the CD36 gene are associated with dietary fat intake, high-fat food consumption and serum triglycerides in a cohort of Quebec adults","author":"T Meng","year":"2021","journal-title":"International Journal of Obesity"},{"issue":"8","key":"pone.0257857.ref067","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1089\/dia.2012.0326","article-title":"CD36 gene variants and their association with type 2 diabetes in an Indian population","volume":"15","author":"S Gautam","year":"2013","journal-title":"Diabetes Technol Ther"},{"issue":"3","key":"pone.0257857.ref068","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1089\/gtmb.2014.0265","article-title":"CD36 gene variants in early prediction of type 2 diabetes mellitus","volume":"19","author":"S Gautam","year":"2015","journal-title":"Genet Test Mol Biomarkers"},{"key":"pone.0257857.ref069","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.gene.2018.03.060","article-title":"CD36 gene variants is associated with type 2 diabetes mellitus through the interaction of obesity in rural Chinese adults","volume":"659","author":"D Zhang","year":"2018","journal-title":"Gene"},{"issue":"7","key":"pone.0257857.ref070","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1634\/theoncologist.2009-0009","article-title":"The role of Src in solid tumors","volume":"14","author":"DL Wheeler","year":"2009","journal-title":"Oncologist"},{"key":"pone.0257857.ref071","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.redox.2016.11.009","article-title":"Rac1-NADPH oxidase signaling promotes CD36 activation under glucotoxic conditions in pancreatic beta cells","volume":"11","author":"S Elumalai","year":"2017","journal-title":"Redox Biology"},{"issue":"6","key":"pone.0257857.ref072","doi-asserted-by":"crossref","first-page":"e99","DOI":"10.1038\/emm.2014.38","article-title":"CD36, a scavenger receptor implicated in atherosclerosis","volume":"46","author":"YM Park","year":"2014","journal-title":"Experimental & Molecular Medicine"},{"issue":"8","key":"pone.0257857.ref073","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.3390\/cells9081877","article-title":"The Multifunctionality of CD36 in Diabetes Mellitus and Its Complications\u2014Update in Pathogenesis, Treatment and Monitoring","volume":"9","author":"K Pucha\u0142owicz","year":"2020","journal-title":"Cells"},{"issue":"16","key":"pone.0257857.ref074","doi-asserted-by":"crossref","first-page":"8428","DOI":"10.1074\/jbc.M115.698365","article-title":"Cytokines Regulate beta-Cell Thioredoxin-interacting Protein (TXNIP) via Distinct Mechanisms and Pathways","volume":"291","author":"K Hong","year":"2016","journal-title":"J Biol Chem"},{"issue":"1","key":"pone.0257857.ref075","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1186\/s12974-019-1498-0","article-title":"NLRP3 inflammasome as a potential treatment in ischemic stroke concomitant with diabetes","volume":"16","author":"P Hong","year":"2019","journal-title":"Journal of Neuroinflammation"},{"issue":"2","key":"pone.0257857.ref076","doi-asserted-by":"crossref","first-page":"222","DOI":"10.4093\/dmj.2020.0053","article-title":"The Role of CD36 in Type 2 Diabetes Mellitus: beta-Cell Dysfunction and Beyond","volume":"44","author":"JS Moon","year":"2020","journal-title":"Diabetes Metab J"},{"issue":"1","key":"pone.0257857.ref077","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1093\/bmb\/ldy006","article-title":"CD36 and lipid metabolism in the evolution of atherosclerosis","volume":"126","author":"L Zhao","year":"2018","journal-title":"British Medical Bulletin"},{"issue":"9","key":"pone.0257857.ref078","doi-asserted-by":"crossref","first-page":"2090","DOI":"10.1161\/ATVBAHA.111.229260","article-title":"Oxidized low-density lipoprotein activates p66Shc via lectin-like oxidized low-density lipoprotein receptor-1, protein kinase C-beta, and c-Jun N-terminal kinase kinase in human endothelial cells","volume":"31","author":"Y Shi","year":"2011","journal-title":"Arterioscler Thromb Vasc Biol"},{"issue":"5","key":"pone.0257857.ref079","first-page":"489","article-title":"Association of cluster of differentiation 36 gene variant rs1761667 (G>A) with metabolic syndrome in Egyptian adults","volume":"33","author":"NM Bayoumy","year":"2012","journal-title":"Saudi Med J"},{"issue":"10","key":"pone.0257857.ref080","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1038\/oby.2012.74","article-title":"Metabolic syndrome is linked to chromosome 7q21 and associated with genetic variants in CD36 and GNAT3 in Mexican Americans","volume":"20","author":"VS Farook","year":"2012","journal-title":"Obesity (Silver Spring)"},{"issue":"5","key":"pone.0257857.ref081","doi-asserted-by":"crossref","first-page":"107","DOI":"10.9790\/0661-155107112","article-title":"Intrusion detection system based on K-star classifier and feature set reduction","volume":"15","author":"DY Mahmood","year":"2013","journal-title":"IOSR Journal of Computer Engineering"},{"issue":"1","key":"pone.0257857.ref082","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1186\/s12911-019-1004-8","article-title":"Comparing different supervised machine learning algorithms for disease prediction","volume":"19","author":"S Uddin","year":"2019","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"pone.0257857.ref083","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"JR Landis","year":"1977","journal-title":"Biometrics"},{"key":"pone.0257857.ref084","doi-asserted-by":"crossref","DOI":"10.1002\/0471445428","volume-title":"Statistical Methods for Rates and Proportions","author":"JL Fleiss","year":"2003","edition":"3"},{"key":"pone.0257857.ref085","doi-asserted-by":"crossref","unstructured":"Ashiquzzaman A, Tushar AK, Islam MR, Shon D, Im K, Park J-H, et al. Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network. 7th iCatse International Conference on IT Convergence and Security; Seoul, South Korea: Springer Singapore; 2017. p. 35\u201343.","DOI":"10.1007\/978-981-10-6451-7_5"},{"key":"pone.0257857.ref086","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1016\/j.procs.2018.05.041","article-title":"Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals","volume":"132","author":"G Swapna","year":"2018","journal-title":"Procedia Computer Science"},{"issue":"1","key":"pone.0257857.ref087","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s12902-019-0436-6","article-title":"Predictive models for diabetes mellitus using machine learning techniques","volume":"19","author":"H Lai","year":"2019","journal-title":"BMC Endocr Disord"},{"issue":"4","key":"pone.0257857.ref088","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1002\/bimj.201500236","article-title":"Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases","volume":"59","author":"T Friede","year":"2017","journal-title":"Biom J"},{"issue":"6","key":"pone.0257857.ref089","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1093\/clinchem\/18.6.499","article-title":"Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge","volume":"18","author":"WT Friedewald","year":"1972","journal-title":"Clin Chem"},{"issue":"1","key":"pone.0257857.ref090","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s12944-021-01439-3","article-title":"Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study","volume":"20","author":"M Niu","year":"2021","journal-title":"Lipids in Health and Disease"},{"issue":"D1","key":"pone.0257857.ref091","doi-asserted-by":"crossref","first-page":"D733","DOI":"10.1093\/nar\/gkv1189","article-title":"Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation","volume":"44","author":"NA O\u2019Leary","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"15","key":"pone.0257857.ref092","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1093\/bioinformatics\/btl268","article-title":"SNPStats: a web tool for the analysis of association studies","volume":"22","author":"X Sole","year":"2006","journal-title":"Bioinformatics"},{"issue":"8","key":"pone.0257857.ref093","doi-asserted-by":"crossref","first-page":"983","DOI":"10.3390\/molecules21080983","article-title":"Bioactive Molecule Prediction Using Extreme Gradient Boosting","volume":"21","author":"I Babajide Mustapha","year":"2016","journal-title":"Molecules"},{"key":"pone.0257857.ref094","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1089\/big.2018.0175","article-title":"Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier: A Review","volume":"7","author":"VBS Prasath","year":"2019","journal-title":"Big Data"},{"issue":"12","key":"pone.0257857.ref095","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1002\/cmdc.200600166","article-title":"Virtual screening for PPAR modulators using a probabilistic neural network","volume":"1","author":"S Derksen","year":"2006","journal-title":"ChemMedChem"},{"issue":"2","key":"pone.0257857.ref096","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.compbiomed.2009.11.014","article-title":"Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction","volume":"40","author":"SL Wang","year":"2010","journal-title":"Comput Biol Med"},{"key":"pone.0257857.ref097","unstructured":"Riedmiller M, Braun H, editors. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. IEEE International Conference on Neural Networks; 1993 28 March-1 April 1993."},{"issue":"1","key":"pone.0257857.ref098","doi-asserted-by":"crossref","first-page":"30","DOI":"10.4258\/hir.2016.22.1.30","article-title":"Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm","volume":"22","author":"W Wiharto","year":"2016","journal-title":"Healthc Inform Res"},{"issue":"5","key":"pone.0257857.ref099","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"JH Friedman","year":"2001","journal-title":"The Annals of Statistics"},{"issue":"23","key":"pone.0257857.ref100","doi-asserted-by":"crossref","DOI":"10.3390\/s19235077","article-title":"A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios","volume":"19","author":"S Chen","year":"2019","journal-title":"Sensors (Basel)"},{"key":"pone.0257857.ref101","unstructured":"K\u00e9gl B. The return of AdaBoost.MH: multi-class Hamming trees: Cornell University; 2013 [14 February 2021]. https:\/\/arxiv.org\/abs\/1312.6086."},{"issue":"3","key":"pone.0257857.ref102","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: the kappa statistic","volume":"22","author":"ML McHugh","year":"2012","journal-title":"Biochem Med (Zagreb)"},{"key":"pone.0257857.ref103","doi-asserted-by":"crossref","first-page":"e260","DOI":"10.1017\/S0950268819001481","article-title":"Applications of machine learning techniques to predict filariasis using socio-economic factors","volume":"147","author":"PK Kondeti","year":"2019","journal-title":"Epidemiol Infect"},{"issue":"5","key":"pone.0257857.ref104","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1007\/s11222-016-9696-4","article-title":"Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC","volume":"27","author":"A Vehtari","year":"2017","journal-title":"Statistics and Computing"},{"key":"pone.0257857.ref105","doi-asserted-by":"crossref","first-page":"b2535","DOI":"10.1136\/bmj.b2535","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"339","author":"D Moher","year":"2009","journal-title":"BMJ"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0257857","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T18:21:50Z","timestamp":1634235710000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0257857"}},"subtitle":[],"editor":[{"given":"Kanhaiya","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":105,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0257857","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,14]]}}}