Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Construction of A Bilirubin Biosensor Based on an Albumin-Immobilized Quartz Crystal Microbalance
(2017) Kocakulak, Mustafa; Bayrak, Tuncay; Saglam, Sinan; 0000-0001-6826-4350; AAE-3731-2020
Bilirubin, a bile pigment, is associated with several diseases and systemic pathologies. The measurement of bilirubin is important for diagnosis and therapy, and many expensive methods are used to measure the bilirubin amount in blood. In this study, a new bilirubin biosensor using quartz crystal microbalances immobilized with albumin is proposed. To measure the effectiveness of the biosensor, a series of experiments was realized with various concentrations of bilirubin, including 1 mg/dL, 2 mg/dL, 5 mg/dL and 10 mg/dL. Comparing blood gas analyzers, laboratory analyzers, skin test devices and nonchemical photometric devices, blood gas analyzers have a range of 0.5-35 mg/dL, laboratory analyzers have a range of 0-30 mg/dL, skin test devices could be used up to 11.7 mg/dL, and nonchemical photometric devices could be evaluated as reliable up to 14.6 mg/dL. The low limit range of the bilirubin detection is between 0.099 mg/dL and 0.146 mg/dL for some special commercial bilirubin measurement devices. Nevertheless, this study presents measurements with a high sensitivity and includes the advantage of reusability by using cheaper materials. To prove albumin immobilization and the bilirubin-albumin interaction atomic force microscopy (AFM) was used, and a good correlation was achieved from AFM images. In conclusion, considering the cost-effectiveness side of the proposed method, a low cost and more sensitive bilirubin measurement device which is effective and reusable was developed instead of the current commercial products. (C) 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Data Integration for Gene Expression Prediction
(2018) Bayrak, Tuncay; Ogul, Hasan; 0000-0001-6826-4350; U-4603-2019
In computational system biology, one challenging topic is predicting the exact value of gene expression for further meta-analysis. For this, a data integration approach and regression based task are proposed. To improve prediction performance, gene expression data consisted of continuous value is integrated with binary data from miRNA-mRNA regulation pairs by a simple approach. For regression task, a recently introduced method, Relevance Vector Machine (RVM) and linear regression are used. For evaluation, Spearman and Pearson Correlation Coefficients, and Root Mean Squared Error are used. The results we obtain show that the proposed approach can significantly improve the prediction performance. Data integration approach and RVM are promising in many machine learning problems.
Microarray Missing Data Imputation Using Regression
(2017) Bayrak, Tuncay; Ogul, Hasan; 0000-0001-6826-4350; U-4603-2019
Having missing values due to several experimental conditions is a common problem in analyzing the results of microarray experiments. Although many imputation methods exist, comparative studies based on regression based models are very limited. Particularly, Relevance Vector Machine (RVM), a recent regression method shown to be effective in various domains, has not been considered so far for missing value imputation in microarray data. In this study, we present a comparative study between regression based models, including linear regression, k-nearest neighbor regression and RVM that uses data obtained from breast, colon and prostate cancer tissues through the microarray technology. The leave-one-out (or Jackknife) procedure is applied for the validation. To measure the performance of the model we used Spearman correlation coefficient (CC). The results reveal that RVM with a Gaussian kernel outperforms other regression models in some cases.
A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering
(2019) Bayrak, Tuncay; Ogul, Hasan; 0000-0001-6826-4350; AAE-3731-2020; AAE-3731-2020
Background: Predicting the value of gene expression in a given condition is a challenging topic in computational systems biology. Only a limited number of studies in this area have provided solutions to predict the expression in a particular pattern, whether or not it can be done effectively. However, the value of expression for the measurement is usually needed for further meta-data analysis. Methods: Because the problem is considered as a regression task where a feature representation of the gene under consideration is fed into a trained model to predict a continuous variable that refers to its exact expression level, we introduced a novel feature representation scheme to support work on such a task based on two-way collaborative filtering. At this point, our main argument is that the expressions of other genes in the current condition are as important as the expression of the current gene in other conditions. For regression analysis, linear regression and a recently popularized method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation coefficients and Root Mean Squared Error are used for evaluation. The effects of regression model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression profiling data comprising a set of prostate cancer samples. Results: According to the findings of this study, in addition to promising results from the experimental studies, integrating data from another disease type, such as colon cancer in our case, can significantly improve the prediction performance of the regression model. Conclusion: The results also showed that the performed new feature representation approach and RVM regression model are promising for many machine learning problems in microarray and high throughput sequencing analysis.