LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index

Cholesterol (CHOL), particularly low-density lipoprotein (LDL) CHOL, is responsible for many important diseases, including coronary artery disease, peripheral artery disease, and heart disease. In today’s modern society, this phenomenon has become a significant health issue for both adult...

Full description

Saved in:
Bibliographic Details
Main Authors: Petros Paplomatas, Marina Nikolidaki, Aristidis Vrahatis, Kostas Anagnostopoulos
Format: Article
Language:English
Published: Academia.edu Journals 2024-12-01
Series:Academia Molecular Biology and Genomics
Online Access:https://www.academia.edu/126364046/Estimation_of_Low_Density_Lipoprotein_LDL_values_using_equations_and_Machine_Learning_and_variance_calculation_of_LDL_and_Atherogenic_Index_of_Plasma_AIP_
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849469557735948288
author Petros Paplomatas
Marina Nikolidaki
Aristidis Vrahatis
Kostas Anagnostopoulos
author_facet Petros Paplomatas
Marina Nikolidaki
Aristidis Vrahatis
Kostas Anagnostopoulos
author_sort Petros Paplomatas
collection DOAJ
description Cholesterol (CHOL), particularly low-density lipoprotein (LDL) CHOL, is responsible for many important diseases, including coronary artery disease, peripheral artery disease, and heart disease. In today’s modern society, this phenomenon has become a significant health issue for both adults and children, primarily due to dietary habits. LDL is a critical risk factor for atherosclerotic vascular disease or cardiovascular disease (CVD). However, the accurate determination of LDL is associated with high costs and technical difficulties such as ultracentrifugation. We present the LDLcalc package, which has two main functionalities: the first is the estimation of LDL from CHOL, high-density lipoprotein (HDL) CHOL, and triglycerides (TG). The second is the determination of the variance of LDL and atherogenic index of plasma (AIP), both of which are calculated clinical chemistry tests. LDL can be estimated using equations published in the literature or through machine learning (ML) methods. The package allows a comprehensive variety of approaches for estimating LDL values in patients, either through equations or through ten ML methods. When the direct measurement of LDL CHOL is not possible, equations or ML methods can be very good stand-ins. Additionally, the package provides the capability to calculate the variance of LDL and AIP using error propagation and bootstrap methods.
format Article
id doaj-art-401bf20e773a4597b32f9ac6fbe1b628
institution Kabale University
issn 3064-9765
language English
publishDate 2024-12-01
publisher Academia.edu Journals
record_format Article
series Academia Molecular Biology and Genomics
spelling doaj-art-401bf20e773a4597b32f9ac6fbe1b6282025-08-20T03:25:26ZengAcademia.edu JournalsAcademia Molecular Biology and Genomics3064-97652024-12-011110.20935/AcadMolBioGen7443LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic indexPetros Paplomatas0Marina Nikolidaki1Aristidis Vrahatis2Kostas Anagnostopoulos3Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.Independent Researcher, Paris 75000, France.Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.Laboratory of Biochemistry, Department of Medicine, Democritus University of Thrace, Alexandroupolis 68100, Greece. Cholesterol (CHOL), particularly low-density lipoprotein (LDL) CHOL, is responsible for many important diseases, including coronary artery disease, peripheral artery disease, and heart disease. In today’s modern society, this phenomenon has become a significant health issue for both adults and children, primarily due to dietary habits. LDL is a critical risk factor for atherosclerotic vascular disease or cardiovascular disease (CVD). However, the accurate determination of LDL is associated with high costs and technical difficulties such as ultracentrifugation. We present the LDLcalc package, which has two main functionalities: the first is the estimation of LDL from CHOL, high-density lipoprotein (HDL) CHOL, and triglycerides (TG). The second is the determination of the variance of LDL and atherogenic index of plasma (AIP), both of which are calculated clinical chemistry tests. LDL can be estimated using equations published in the literature or through machine learning (ML) methods. The package allows a comprehensive variety of approaches for estimating LDL values in patients, either through equations or through ten ML methods. When the direct measurement of LDL CHOL is not possible, equations or ML methods can be very good stand-ins. Additionally, the package provides the capability to calculate the variance of LDL and AIP using error propagation and bootstrap methods.https://www.academia.edu/126364046/Estimation_of_Low_Density_Lipoprotein_LDL_values_using_equations_and_Machine_Learning_and_variance_calculation_of_LDL_and_Atherogenic_Index_of_Plasma_AIP_
spellingShingle Petros Paplomatas
Marina Nikolidaki
Aristidis Vrahatis
Kostas Anagnostopoulos
LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index
Academia Molecular Biology and Genomics
title LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index
title_full LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index
title_fullStr LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index
title_full_unstemmed LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index
title_short LDL estimation with equations and machine learning; variance of LDL and plasma atherogenic index
title_sort ldl estimation with equations and machine learning variance of ldl and plasma atherogenic index
url https://www.academia.edu/126364046/Estimation_of_Low_Density_Lipoprotein_LDL_values_using_equations_and_Machine_Learning_and_variance_calculation_of_LDL_and_Atherogenic_Index_of_Plasma_AIP_
work_keys_str_mv AT petrospaplomatas ldlestimationwithequationsandmachinelearningvarianceofldlandplasmaatherogenicindex
AT marinanikolidaki ldlestimationwithequationsandmachinelearningvarianceofldlandplasmaatherogenicindex
AT aristidisvrahatis ldlestimationwithequationsandmachinelearningvarianceofldlandplasmaatherogenicindex
AT kostasanagnostopoulos ldlestimationwithequationsandmachinelearningvarianceofldlandplasmaatherogenicindex