Prediction of VLDL cholesterol value with machine learning techniques

Cholesterol is an oil-like substance that is found in the membranes of animal cells and also carried in blood plasma, which has some vital functions in the human body, especially in the endocrine and digestive systems. Very Low-Density Lipoprotein (VLDL) is a lipid that is not gained with nutrients, instead produced by the body itself. However, it is considered to be in the bad cholesterol group since this type of cholesterol threatens cardiovascular health. As a result, it is normally expected to be at the lowest levels in the human body. In this study, It is applied some machine learning techniques to estimate VLDL Cholesterol value by some attributes such as age, sex, creatinine, aspartat transaminaz (AST), alanine transaminaz (ALT), free t4, glucose, and triglyceride. In this, the techniques include the Generalized Linear Model (GLM), Decision Tree (DT), and Gradient Boosted Trees (GBT). It is computed that GLM has the root-mean-squared-error value 0.655 and the correlation value 1.0 so consequently returns the best results compared to others.No sponso

Instance-based regression by partitioning feature projections

Ankara : Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2000.Thesis (Master's) -- Bilkent University, 2000.Includes bibliographical references leaves 87-92.Uysal,İlhanM.S

Development of a Simulation Environment for the Importance of Histone Deacetylase in Childhood Acute Leukemia with Explainable Artificial Intelligence

This study aims to explore new therapeutic opportunities for histone deacetylase (HDAC) inhibitors by leveraging drug repurposing approaches and analyzing their bioactivity and molecular fingerprints. The methodology includes investigating drug repurposing opportunities for HDAC inhibitors, evaluating the bioactivity of repurposing drugs on HDAC enzymes, investigating the role of HDAC genes in therapeutic effects, and analyzing molecular fingerprints with explainable artificial intelligence (XAI) to identify structurally similar compounds with potential HDAC inhibitory activity. In this context, chemical compounds with IC50 (7903 compounds) and Inhibition (1084 compounds) standard types of HDAC genes reported to be associated with childhood acute leukemia were represented by molecular fingerprints. Regression and classification models were applied to the molecular fingerprints, and the results obtained were supported by XAI. All the study results were shared interactively on the website address https://iuysal1905-childhoodacuteleukemia-drug-interacito-arayuz-r89zld.streamlit.app/ by designing a simulation environment. The influence of molecular fingerprints on the models and their impact on potential drug development in childhood acute leukemia were evaluated using XAI techniques, particularly through the analysis of SHAP values. The study contributes to the literature on the use of XAI technology in drug repurposing studies, especially in cancer, the study of molecular properties, and the active use of XAI in drug repurposing studies

Shear Bond Strength Evaluation of Different Composites Used As Lingual Retainer Adhesives

The aim of this study was to determine the shear bond strength (SBS) levels and fracture modes of different composites used as lingual retainer adhesives. Sixty human mandibular incisors were used, that mounted in acrylic resin leaving the buccal surface of the crowns parallel to the base of the moulds. Randomly three groups were constructed, each containing 20 teeth. Transbond-LR (3M-Uni-tek), Transbond-XT (3M-Unitek) and Venus Flow (Heraeus Kulzer) were tested. Materials were applied to the teeth surface by packing the material into the cylindrical plastic matrices with a 2.34 mm internal diameter and a 3 mm height (Ultradent) to simulate the lingual retainer bonding. For SBS testing, the specimens were mounted in a universal testing machine, and an apparatus (Ultradent) attached to a compression load cell was applied to each of the specimen until the failure occurred. The SBS data were analyzed using analysis of variance and Tukey tests, and chi-square test was used to analyze the fracture modes. The statistical tests indicated that Transbond-LR shows statistically significant higher SBS (24.7±9.25 MPa) then Transbond-XT (12.01±4.98 MPa) and Venus Flow (14.07±5.25 MPa) (P<0.001) whereas the difference between Transbond-XT and Venus Flow was not significant. In general, a greater percentage of the fractures were adhesive at the tooth-composite interface (60%% for Transbond-LR and Venus Flow and 90°% for Transbond-XT) and no statistically significant difference was found between the groups. According to the results of this study, Transbond LR was found to be most appropriate material for the tested specification

Evaluation of Fit between Tooth and Band Surfaces when Different Orthodontic Cements are Used

The aim of this in vitro study was to compare three orthodontic band cements for gaps remained between tooth and band surfaces, at the cervical margin, which possibly caused after banding procedure. Sixty freshly extracted human mandibular third molars were randomly divided into 3 equal groups. Micro-etched molar bands were cemented to the teeth using each of the orthodontic band cements (Ketac Cem®, 3M Multi-Cure® and Transbond Plus®). The teeth were placed in preformed boxes (2.5X2.5X2.5 cm); crowns were on the bottom and perpendicular to the ground. Samples were capped with black colored plaster on a vibration machine. After hardening of the plasters, samples were removed from the boxes and were trimmed in the bucco-lingual dimension for evaluation. The mean of four parallel sections examined under a stere-omicroscope was noted as the score of that sample, for buccal and lingual sides, separately. Statistically analyses were performed by using analysis of variance and paired-samples t-test. When buccal and lingual gap formations were compared, Transbond Plus® and Ketac Cem® groups were found to have larger gaps in the lingual side than buccal at p<0.05 and p<0.01 level, respectively. Thus, buccal and lingual gaps of three cements were compared separately, and no statistically significant difference was determined among three investigated cement types at buccal and lingual sides. However no differences were found among different types of band cements, it is clear that large gaps were observed under molar bands at cervical margin; where it is not easily possible to clean. Further studies should be conducted to determine a favorable strategy to eliminate these gaps and maintain a gap-free adaptation and cementation between band and tooth structure

Hasarlı kompozit plakların onarımı

Bu çalışmanın amacı, onarılmış cam-epoksi kompozit plakların mekanik performanslarının deneysel ve sayısal yolla incelenmesidir. Deney esnasında kullanılan numuneler, vakum destekli reçine infüzyon sistemi kullanılarak Dokuz Eylül Üniversitesi Kompozit Araştırma Laboratuarı'nda üretilmiştir. Numunelerin üretiminde, örgülü cam-fiber kumaşlar kullanılmıştır. Deneylerde Shimadzu AUTOGRAPH AG-IS Serisi üniversal çekme testi makinası kullanılmıştır. Sayısal analizlerde LUSAS 14.1 yazılımından yararlanılmıştır. Yamasız, iki basamaklı yamalı ve üç basamaklı yamalı olmak üzere üç farklı çekme numunesi deneysel ve sayısal olarak incelenmiştir. Bunun dışında çekme testi standartlarına uygun farklı tiplerde çekme numuneleri LUSAS'la modellenip analiz edilmiştir. Süreksiz bölgeleri simüle etmek için arayüzey elemanları kullanılmıştır. Analiz sonuçları değerlendirilip tartışılmıştır. Son olarak eğilme testi standartlarına uygun, farklı tiplerde eğilme numuneleri de LUSAS ile analiz edilmiştir. Çalışmadan elde edilen sonuçlar, sonuçlar kısmında özetlenmiştir. The aim of this study was to examine the mechanical performance of the repaired glass-epoxy composite laminates experimentally and numerically and comparing the results with eachother. The specimens used in experiments were manufactured by vacuum assisted resin infusion molding method in Composite Research Laboratory at Dokuz Eylul University. Woven glass fabrics were used in fabrication of the specimens. Shimadzu AUTOGRAPH AG-IS Series universal tensile test machine was used in the experiments. In the numerical analysis, LUSAS 14.1 software was utilized. Three different types of tensile specimens, i.e. unpatched, two stepped patched and three stepped patched specimens, were examined experimentally and numerically. Besides this, different types of tensile specimens, according to the tensile test standards, are modelled and analyzed with LUSAS. Interface elements were used to simulate uncontinuous regions in the patched specimens. Analysis results are evaluated and discussed. Eventually, different types of bending specimens, according to the three point bending test standards, are also analyzed with LUSAS followed by the discussions. The conclusions drawn from the study are summarized in conclusions section

Prediction of VLDL cholesterol value with machine learning techniques

Cholesterol is an oil-like substance that is found in the membranes of animal cells and also carried in blood plasma, which has some vital functions in the human body, especially in the endocrine and digestive systems. Very Low-Density Lipoprotein (VLDL) is a lipid that is not gained with nutrients, instead produced by the body itself. However, it is considered to be in the bad cholesterol group since this type of cholesterol threatens cardiovascular health. As a result, it is normally expected to be at the lowest levels in the human body. In this study, It is applied some machine learning techniques to estimate VLDL Cholesterol value by some attributes such as age, sex, creatinine, aspartat transaminaz (AST), alanine transaminaz (ALT), free t4, glucose, and triglyceride. In this, the techniques include the Generalized Linear Model (GLM), Decision Tree (DT), and Gradient Boosted Trees (GBT). It is computed that GLM has the root-mean-squared-error value 0.655 and the correlation value 1.0 so consequently returns the best results compared to others.No sponso

Instance-Based Regression by Partitioning Feature Projections

A new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional method, KNN, is very popular for classification. Although KNN performs well on classification tasks, it does not perform as well on regression problems. We have developed a new instance-based method, called Regression by Partitioning Feature Projections (RPFP) which is designed to meet the requirement for a lazy method that achieves high levels of accuracy on regression problems. RPFP gives better performance than well-known eager approaches found in machine learning and statistics such as MARS, rule-based regression, and regression tree induction systems. The most important property of RPFP is that it is a projectionbased approach that can handle interactions. We show that it outperforms existing eager or lazy approaches on many domains when there are many missing values in the training data