{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:22:40Z","timestamp":1777890160952,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"3-4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MGS"],"published-print":{"date-parts":[[2023,2,3]]},"abstract":"<jats:p>Patients with chronic liver diseases typically experience lipid profile problems, and mortality from cirrhosis complicated by portal vein thrombosis (PVT) is very significant. A lipoprotein (Lp) is a bio-chemical assemblage with the main job of moving fat molecules in water that are hydrophobic. Lipoproteins are present in all eubacterial walls. Lipoproteins are of tremendous interest in the study of spirochaetes\u2019 pathogenic mechanisms. Since spirochaete lipobox sequences are more malleable than other bacteria, it\u2019s proven difficult to apply current prediction methods to new sequence data. The major goal is to present a Lipoprotein detection model in which correlation features, enhanced log energy entropy, raw features, and semantic similarity features are extracted. These extracted characteristics are put through a hybrid model that combines a Gated Recurrent Unit (GRU) and a Long Short-Term Memory (LSTM). Then, the outputs of GRU and LSTM are averaged to obtain the output. Here, GRU weights are optimized via the Selfish combined Henry Gas Solubility Optimization with cubic map initialization (SHGSO) model.<\/jats:p>","DOI":"10.3233\/mgs-220329","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T11:59:22Z","timestamp":1675166362000},"page":"345-363","source":"Crossref","is-referenced-by-count":2,"title":["Lipoprotein detection: Hybrid deep classification model with improved feature set"],"prefix":"10.1177","volume":"18","author":[{"given":"Pravin Narayan","family":"Kathavate","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Amudhavel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/MGS-220329_ref1","doi-asserted-by":"crossref","unstructured":"B. Gao, J. Xiao and M. Zhang, High-density lipoprotein cholesterol for the prediction of mortality in cirrhosis with portal vein thrombosis: A retrospective study, Lipids Health Dis 18 (2019).","DOI":"10.1186\/s12944-019-1005-8"},{"key":"10.3233\/MGS-220329_ref3","doi-asserted-by":"crossref","unstructured":"Z. Liu, Q. Fan and S. Wu, Compared with the monocyte to high-density lipoprotein ratio (MHR) and the neutrophil to lymphocyte ratio (NLR), the neutrophil to high-density lipoprotein ratio (NHR) is more valuable for assessing the inflammatory process in Parkinson\u2019s disease, Lipids Health Dis 20 (2021).","DOI":"10.1186\/s12944-021-01462-4"},{"key":"10.3233\/MGS-220329_ref4","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s12265-019-09908-y","article-title":"Oxidised low-density lipoprotein and its receptor-mediated endothelial dysfunction are associated with coronary artery lesions in kawasaki disease","volume":"13","author":"He","year":"2020","journal-title":"J. of Cardiovasc. Trans. Res."},{"key":"10.3233\/MGS-220329_ref5","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1007\/s43657-021-00021-2","article-title":"Prediction of Metabolic Disorders Using NMR-Based Metabolomics: The Shanghai Changfeng Study","volume":"1","author":"Wu","year":"2021","journal-title":"Phenomics"},{"key":"10.3233\/MGS-220329_ref6","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1186\/s12944-020-01353-0","article-title":"Association between carotid intima media thickness and small dense low-density lipoprotein cholesterol in acute ischaemic stroke","volume":"19","author":"Zhou","year":"2020","journal-title":"Lipids Health Dis"},{"key":"10.3233\/MGS-220329_ref7","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13098-017-0221-5","article-title":"Impact of menopause and diabetes on atherogenic lipid profile: Is it worth to analyse lipoprotein subfractions to assess cardiovascular risk in women","volume":"9","author":"Fonseca","year":"2017","journal-title":"Diabetol Metab Syndr"},{"key":"10.3233\/MGS-220329_ref8","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1186\/s12887-021-02875-x","article-title":"Rare novel LPL mutations are associated with neonatal onset lipoprotein lipase (LPL) deficiency in two cases","volume":"21","author":"Wu","year":"2021","journal-title":"BMC Pediatr"},{"key":"10.3233\/MGS-220329_ref9","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s00380-018-1247-9","article-title":"Increased triglyceride\/high-density lipoprotein cholesterol ratio may be associated with reduction in the low-density lipoprotein particle size: Assessment of atherosclerotic cardiovascular disease risk","volume":"34","author":"Yokoyama","year":"2019","journal-title":"Heart Vessels"},{"key":"10.3233\/MGS-220329_ref10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s12944-020-01240-8","article-title":"The predictive study of the relation between elevated low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and mortality in peritoneal dialysis","volume":"19","author":"Lin","year":"2020","journal-title":"Lipids Health Dis"},{"key":"10.3233\/MGS-220329_ref11","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s11596-020-2263-4","article-title":"Effects of pitavastatin on lipoprotein subfractions and oxidized low-density lipoprotein in patients with atherosclerosis","volume":"40","author":"Xu","year":"2020","journal-title":"Curr Med Sci"},{"key":"10.3233\/MGS-220329_ref12","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1186\/s12872-021-02129-9","article-title":"Non-high-density lipoprotein cholesterol\/high-density lipoprotein cholesterol ratio serve as a predictor for coronary collateral circulation in chronic total occlusive patients","volume":"21","author":"Li","year":"2021","journal-title":"BMC Cardiovasc Disord"},{"key":"10.3233\/MGS-220329_ref13","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s13340-015-0208-0","article-title":"Non-high-density cholesterol level as a predictor of maximum carotid intima-media thickness in Japanese subjects with type 2 diabetes: A comparison with low-density lipoprotein level","volume":"7","author":"Bando","year":"2016","journal-title":"Diabetol Int"},{"key":"10.3233\/MGS-220329_ref14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1186\/s12883-021-02472-6","article-title":"Association between small dense low-density lipoprotein cholesterol and neuroimaging markers of cerebral small vessel disease in middle-aged and elderly Chinese populations","volume":"21","author":"Yu","year":"2021","journal-title":"BMC Neurol"},{"key":"10.3233\/MGS-220329_ref15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s12944-017-0451-4","article-title":"Relationship between non-high-density lipoprotein cholesterol and carotid atherosclerosis in normotensive and euglycemic Chinese middle-aged and elderly adults","volume":"16","author":"Ma","year":"2017","journal-title":"Lipids Health Dis"},{"key":"10.3233\/MGS-220329_ref16","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1007\/s00592-018-1195-y","article-title":"Fasting plasma glucose is a stronger predictor of diabetes than triglyceride-glucose index, triglycerides\/high-density lipoprotein cholesterol, and homeostasis model assessment of insulin resistance: Tehran lipid and glucose study","volume":"55","author":"Tohidi","year":"2018","journal-title":"Acta Diabetol"},{"key":"10.3233\/MGS-220329_ref17","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s10557-019-06906-9","article-title":"Lipoprotein(a) and atherosclerotic cardiovascular disease: Current understanding and future perspectives","volume":"33","author":"Wu","year":"2019","journal-title":"Cardiovasc Drugs Ther"},{"key":"10.3233\/MGS-220329_ref18","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jacc.2018.04.060","article-title":"Lipoprotein(a) and cardiovascular risk prediction among women","volume":"72","author":"Cook","year":"2018","journal-title":"Journal of the American College of Cardiology"},{"key":"10.3233\/MGS-220329_ref19","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1016\/S0140-6736(18)31652-0","article-title":"Baseline and on-statin treatment lipoprotein(a) levels for prediction of cardiovascular events: Individual patient-data meta-analysis of statin outcome trials","volume":"392","author":"Willeit","year":"2018","journal-title":"The Lancet"},{"key":"10.3233\/MGS-220329_ref20","doi-asserted-by":"crossref","unstructured":"Q. Liang, X. Lei, X. Huang, L. Fan and H. Yu, Elevated Lipoprotein-Associated Phospholipase A2 is Valuable in Prediction of Coronary Slow Flow in Non-ST-Segment Elevation Myocardial Infarction Patients, Current Problems in Cardiology 46 (2020).","DOI":"10.1016\/j.cpcardiol.2020.100596"},{"key":"10.3233\/MGS-220329_ref21","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.cca.2017.11.028","article-title":"Early prediction of persistent organ failure by serum apolipoprotein A-I and high-density lipoprotein cholesterol in patients with acute pancreatitis","volume":"476","author":"Zhou","year":"2018","journal-title":"Clinica Chimica Acta January"},{"key":"10.3233\/MGS-220329_ref22","doi-asserted-by":"crossref","unstructured":"A. Chakraborty, D.C. Chan, K.L. Ellis, J. Pang, W. Barnett, A.M. Woodward and G.F. Watts, Cascade testing for elevated lipoprotein(a) in relatives of probands with high lipoprotein(a), American Journal of Preventive Cardiology 10 (2022).","DOI":"10.1016\/j.ajpc.2022.100343"},{"key":"10.3233\/MGS-220329_ref24","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.amjcard.2020.03.037","article-title":"Additive Value of High-Density Lipoprotein Cholesterol and C-Reactive Protein Level Assessment for Prediction of 2-year Mortality After Transcatheter Aortic Valve Implantation","volume":"126","author":"Zieli\u0144ski","year":"2020","journal-title":"The American Journal of Cardiology"},{"key":"10.3233\/MGS-220329_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atherosclerosis.2018.12.003","article-title":"Systematic prediction of familial hypercholesterolemia caused by low-density lipoprotein receptor missense mutations","volume":"281","author":"Guo","year":"2019","journal-title":"Atherosclerosis"},{"key":"10.3233\/MGS-220329_ref27","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1007\/s10439-009-9795-x","article-title":"Log energy entropy-based EEG classification with multilayer neural networks in seizure","volume":"37","author":"Ayd\u0131n","year":"2009","journal-title":"Annals of Biomedical Engineering"},{"key":"10.3233\/MGS-220329_ref29","doi-asserted-by":"crossref","unstructured":"X. Li, X. Ma, F. Xiao, C. Xiao, F. Wang and S. Zhang, Time-series production forecasting method based on the integration of Bidirectional Gated Recurrent Unit (Bi-GRU) network and Sparrow Search Algorithm (SSA), Journal of Petroleum Science and Engineering 208 (2022).","DOI":"10.1016\/j.petrol.2021.109309"},{"key":"10.3233\/MGS-220329_ref31","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.future.2019.07.015","article-title":"Henry gas solubility optimization: A novel physics-based algorithm","volume":"101","author":"Hashim","year":"2019","journal-title":"Future Generation Computer Systems"},{"key":"10.3233\/MGS-220329_ref32","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.biosystems.2017.07.010","article-title":"A global optimization algorithm inspired in the behavior of selfish herds","volume":"160","author":"Fausto","year":"2017","journal-title":"Biosystems"},{"key":"10.3233\/MGS-220329_ref33","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1002\/ima.22087","article-title":"Threshold prediction for segmenting tumour from brain MRI scans","volume":"24","author":"Marsaline Beno","year":"2014","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"10.3233\/MGS-220329_ref34","first-page":"33","article-title":"Hybrid optimization based DBN for face recognition using low-resolution images","volume":"1","author":"Thomas","year":"2018","journal-title":"Multimedia Research"},{"key":"10.3233\/MGS-220329_ref35","first-page":"31","article-title":"Optimal resource allocation of cluster using hybrid grey wolf and cuckoo search algorithm in cloud computing","volume":"3","author":"Devagnanam","year":"2020","journal-title":"Journal of Networking and Communication Systems"},{"key":"10.3233\/MGS-220329_ref36","first-page":"26","article-title":"A hybrid learning algorithm for optimal reactive power dispatch under unbalanced conditions","volume":"1","author":"Mahammad Shareef","year":"2018","journal-title":"Journal of Computational Mechanics, Power System and Control"},{"key":"10.3233\/MGS-220329_ref37","first-page":"453","article-title":"Classification and Prediction of Low-Density Lipoprotein Cholesterol LDL-C in The Palestinian Patients Using Machine Learning Techniques, Intelligent Networks and Systems Society (INASS)","volume":"15","author":"Sanad Awad","year":"2022","journal-title":"International Journal of Intelligent Engineering and Systems"},{"key":"10.3233\/MGS-220329_ref38","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1093\/labmed\/lmab065","article-title":"Estimation of low-density lipoprotein cholesterol concentration using machine learning","volume":"53","author":"C.\u00c7ubuk\u00e7u","year":"2022","journal-title":"Lab Med"},{"key":"10.3233\/MGS-220329_ref42","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3390\/technologies10010017","article-title":"Stacking-based ensemble learning method for multi-spectral image classification","volume":"10","author":"Tagel","year":"2022","journal-title":"Technologies"},{"key":"10.3233\/MGS-220329_ref43","doi-asserted-by":"crossref","first-page":"283","DOI":"10.4258\/hir.2019.25.4.283","article-title":"Stacking ensemble technique for classifying breast cancer","volume":"25","author":"Hyunjin","year":"2019","journal-title":"Healthcare Informatics Research"},{"key":"10.3233\/MGS-220329_ref44","doi-asserted-by":"crossref","unstructured":"S.A.N. Alexandropoulos, C.K. Aridas, S.B. Kotsiantis and M.N. Vrahatis, Stacking strong ensembles of classifiers, in: IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer, Cham, 2019.","DOI":"10.1007\/978-3-030-19823-7_46"},{"key":"10.3233\/MGS-220329_ref46","doi-asserted-by":"crossref","unstructured":"Z. Li, D. Cai, J. Wang, J. Fu, L. Qin and D. Fu, A Stacking Ensemble Learning Model for Mobile Traffic Prediction, in: 2020 IEEE\/CIC International Conference on Communications in China (ICCC), IEEE, 2020.","DOI":"10.1109\/ICCC49849.2020.9238996"}],"container-title":["Multiagent and Grid Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/MGS-220329","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:28:58Z","timestamp":1777613338000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/MGS-220329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":37,"journal-issue":{"issue":"3-4"},"URL":"https:\/\/doi.org\/10.3233\/mgs-220329","relation":{},"ISSN":["1875-9076","1574-1702"],"issn-type":[{"value":"1875-9076","type":"electronic"},{"value":"1574-1702","type":"print"}],"subject":[],"published":{"date-parts":[[2023,2,3]]}}}