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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes(Springer, 2019) Department of Business Administration; Department of Industrial Engineering; N/A; Tan, Barış; Dizbin, Nima Manafzadeh; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; Graduate School of Business; 28600; N/AEmpirical studies show that the inter-event times of a production system are correlated. However, most of the analytical studies for the analysis and control of production systems ignore correlation. In this study, we show that real-time data collected from a manufacturing system can be used to build a Markovian arrival processes (MAP) model that captures correlation in inter-event times. The obtained MAP model can then be used to control production in an effective way. We first present a comprehensive review on MAP modeling and MAP fitting methods applicable to manufacturing systems. Then we present results on the effectiveness of these fitting methods and discuss how the collected inter-event data can be used to represent the flow dynamics of a production system accurately. In order to study the impact of capturing the flow dynamics accurately on the performance of a production control system, we analyze a manufacturing system that is controlled by using a base-stock policy. We study the impact of correlation in inter-event times on the optimal base-stock level of the system numerically by employing the structural properties of the MAP. We show that ignoring correlated arrival or service process can lead to overestimation of the optimal base-stock level for negatively correlated processes, and underestimation for the positively correlated processes. We conclude that MAPs can be used to develop data-driven models and control manufacturing systems more effectively by using shop-floor inter-event data.Publication Open Access Discovery of a small molecule that selectively destabilizes Cryptochrome 1 and enhances life span in p53 knockout mice(Nature Portfolio, 2022) Akyel, Yasemin Kübra; Korkmaz, Tuba; Selvi, Saba; Danış, İbrahim; İpek, Özgecan Savluğ; Aygenli, Fatih; Öztürk, Nuri; Öztürk, Narin; Ünal, Durişehvar Özer; Güzel, Mustafa; Okyar, Alper; N/A; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Gül, Şeref; Gül, Zeynep Melis; Işın, Şafak; Özcan, Onur; Akarlar, Büşra; Taşkın, Ali Cihan; Türkay, Metin; Kavaklı, İbrahim Halil; Researcher; Other; Faculty Member; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; N/A; N/A; N/A; N/A; N/A; 291296; 105301; 24956; 40319Cryptochromes are negative transcriptional regulators of the circadian clock in mammals. It is not clear how reducing the level of endogenous CRY1 in mammals will affect circadian rhythm and the relation of such a decrease with apoptosis. Here, we discovered a molecule (M47) that destabilizes Cryptochrome 1 (CRY1) both in vitro and in vivo. The M47 selectively enhanced the degradation rate of CRY1 by increasing its ubiquitination and resulted in increasing the circadian period length of U2OS Bmal1-dLuc cells. In addition, subcellular fractionation studies from mice liver indicated that M47 increased degradation of the CRY1 in the nucleus. Furthermore, M47-mediated CRY1 reduction enhanced oxaliplatin-induced apoptosis in Ras-transformed p53 null fibroblast cells. Systemic repetitive administration of M47 increased the median lifespan of p53(-/-) mice by similar to 25%. Collectively our data suggest that M47 is a promising molecule to treat forms of cancer depending on the p53 mutation.Publication Open Access Customer mobility signatures and financial indicators as predictors in product recommendation(Public Library of Science, 2018) Bozkaya, Burçin; Department of Industrial Engineering; Ürküp, Çağan; Salman, Fatma Sibel; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 178838The rapid growth of mobile payment and geo-aware systems as well as the resulting emergence of Big Data present opportunities to explore individual consuming patterns across space and time. Here we analyze a one-year transaction dataset of a leading commercial bank to understand to what extent customer mobility behavior and financial indicators can predict the use of a target product, namely the Individual Consumer Loan product. After data preprocessing, we generate 13 datasets covering different time intervals and feature groups, and test combinations of 3 feature selection methods and 10 classification algorithms to determine, for each dataset, the best feature selection method and the most influential features, and the best classification algorithm. We observe the importance of spatio-temporal mobility features and financial features, in addition to demography, in predicting the use of this exemplary product with high accuracy (AUC = 0.942). Finally, we analyze the classification results and report on most interesting customer characteristics and product usage implications. Our findings can be used to potentially increase the success rates of product recommendation systems.Publication Open Access Assessment of quarter billion primary care prescriptions from a nationwide antimicrobial stewardship program(Nature Publishing Group (NPG), 2021) Aksoy, Mesil; İşli, Fatma; Gürpınar, Umut Emre; Göbel, Pınar; Gürsöz, Hakkı; Department of Industrial Engineering; Ergönül, Önder; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; 110398; 237468We described the significance of systematic monitoring nationwide antimicrobial stewardship programs (ASPs) in primary care. All the prescriptions given by family physicians were recorded in Prescription Information System established by the Turkish Medicines and Medical Devices Agency of Ministry of Health. We calculated, for each prescription, ""antibiotics amount"" as number of boxes times number of items per box for medicines that belong to antiinfectives for systemic use (i.e., J01 block in the Anatomical Therapeutic Chemical Classification System). We compared the antibiotics amount before (2015) and after (2016) the extensive training programs for the family physicians. We included 266,389,209 prescriptions from state-operated family healthcare units (FHUs) between January 1, 2015 and December 31, 2016. These prescriptions were given by 26,313 individual family physicians in 22,518 FHUs for 50,713,181 individual patients. At least one antimicrobial was given in 37,024,232 (28.31%) prescriptions in 2015 and 36,154,684 (26.66%) prescriptions in 2016. The most common diagnosis was ""acute upper respiratory infections (AURI)"" (i.e., J00-J06 block in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems) with 28.05%. The average antibiotics amount over prescriptions with AURI decreased in 79 out of 81 provinces, and overall rate of decrease in average antibiotics amount was 8.33%, where 28 and 53 provinces experienced decreases (range is between 28.63% and-3.05%) above and below this value, respectively. In the most successful province, the highest decrease in average amount of ""other beta-lactam antibacterials"" per prescription for AURI was 49.63% in January. Computational analyses on a big data set collected from a nationwide healthcare system brought a significant contribution in improving ASPs.Publication Open Access Optimization based tumor classification from microarray gene expression data(Public Library of Science, 2011) Üney-Yüksektepe, Fadime; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Dağlıyan, Onur; Kavaklı, İbrahim Halil; Türkay, Metin; Master Student; Faculty Member; Department of Chemical and Biological Engineering; Department of Industrial Engineering; College of Engineering; N/A; 40319; 24956Background: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. Methodology/Principal Findings: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. Conclusions/Significance: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.Publication Open Access Structure-based design and classifications of small molecules regulating the circadian rhythm period(Nature Portfolio, 2021) Yılmaz, Fatma; Öztürk, Nuri; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Molecular Biology and Genetics; Türkay, Metin; Rahim, Fatih; Gül, Şeref; Kavaklı, İbrahim Halil; Işın, Şafak; Faculty Member; Researcher; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Molecular Biology and Genetics; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/A; 40319; N/ACircadian rhythm is an important mechanism that controls behavior and biochemical events based on 24 h rhythmicity. Ample evidence indicates disturbance of this mechanism is associated with different diseases such as cancer, mood disorders, and familial delayed phase sleep disorder. Therefore, drug discovery studies have been initiated using high throughput screening. Recently the crystal structures of core clock proteins (CLOCK/BMAL1, Cryptochromes (CRY), Periods), responsible for generating circadian rhythm, have been solved. Availability of structures makes amenable core clock proteins to design molecules regulating their activity by using in silico approaches. In addition to that, the implementation of classification features of molecules based on their toxicity and activity will improve the accuracy of the drug discovery process. Here, we identified 171 molecules that target functional domains of a core clock protein, CRY1, using structure-based drug design methods. We experimentally determined that 115 molecules were nontoxic, and 21 molecules significantly lengthened the period of circadian rhythm in U2OS cells. We then performed a machine learning study to classify these molecules for identifying features that make them toxic and lengthen the circadian period. Decision tree classifiers (DTC) identified 13 molecular descriptors, which predict the toxicity of molecules with a mean accuracy of 79.53% using tenfold cross-validation. Gradient boosting classifiers (XGBC) identified 10 molecular descriptors that predict and increase in the circadian period length with a mean accuracy of 86.56% with tenfold cross-validation. Our results suggested that these features can be used in QSAR studies to design novel nontoxic molecules that exhibit period lengthening activity.Publication Open Access Ultrasensitive proteomic quantitation of cellular signaling by digitized nanoparticle-protein counting(Nature Publishing Group (NPG), 2016) Jacob, Thomas; Agarwal, Anupriya; Ramunno-Johnson, Damien; O'Hare, Thomas; Tyner, Jeffrey W.; Druker, Brian J.; Vu, Tania Q.; Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468Many important signaling and regulatory proteins are expressed at low abundance and are difficult to measure in single cells. We report a molecular imaging approach to quantitate protein levels by digitized, discrete counting of nanoparticle-tagged proteins. Digitized protein counting provides ultrasensitive molecular detection of proteins in single cells that surpasses conventional methods of quantitating total diffuse fluorescence, and offers a substantial improvement in protein quantitation. We implement this digitized proteomic approach in an integrated imaging platform, the single cell-quantum dot platform (SC-QDP), to execute sensitive single cell phosphoquantitation in response to multiple drug treatment conditions and using limited primary patient material. The SC-QDP: 1) identified pAKT and pERK phospho-heterogeneity and insensitivity in individual leukemia cells treated with a multi-drug panel of FDA-approved kinase inhibitors, and 2) revealed subpopulations of drug-insensitive CD34+ stem cells with high pCRKL and pSTAT5 signaling in chronic myeloid leukemia patient blood samples. This ultrasensitive digitized protein detection approach is valuable for uncovering subtle but important differences in signaling, drug insensitivity, and other key cellular processes amongst single cells.Publication Open Access Comparison of the results of blood glucose self-monitoring and continuous glucose monitoring in pregnant women with previous diabetes mellitus(Moscow Region Research and Clinical Institute (MONIKI), 2015) Dreval, A. V.; Shestakova, T. P.; Dreval, O. A.; Kulikov, D. A.; Medvedev, O. S.; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956Background: Pregnancy is one of the indications for continuous glucose monitoring (CGM). The data on its efficiency in pregnant women are contradictory. Aim: To compare the results of blood glucose self-monitoring (SMBG) and CGM in pregnant women with previous diabetes mellitus. Materials and methods: We performed a cross-sectional comparative study of glycemia in 18 pregnant women with previous type 1 (87.8% of patients) and type 2 diabetes (22.2% of patients) with various degrees of glycemic control. Their age was 27.7 ± 4.9 year. At study entry, the patients were at 17.2 ± 6.1 weeks of gestation. CGM and SMBG were performed in and by all patients for the duration of 5.4 ± 1.5 days. Depending on their hba1c levels, all patients were divided into two groups: group 1 – 12 women with the hba1c above the target (8.5 ± 1%), and group 2 – 6 women with the hba1c levels within the target (5.6 ± 0.3%). Results: According to SMBG results, women from group 2 had above-the-target glycemia levels before breakfast, at 1 hour after breakfast and at bedtime: 6.2 ± 1.6, 8.7 ± 2.1, and 5.7 ± 1.9 mmol/L, respectively. According to CGM, patients from group 1 had higher postprandial glycemia than those from group 2 (8.0 ± 2.1 and 6.9 ± 1.8 mmol/L, respectively, p = 0.03). The analysis of glycemia during the day time revealed significant difference between the groups only at 1 hour after dinner (7.1 ± 1.4 mmol/L in group 1 and 5.8 ± 0.9 mmol/L in group 2, р = 0.041) and the difference was close to significant before lunch (6.0 ± 2.2 mmol/L in group 1 and 4.8 ± 1.0 mmol/L in group 2, р = 0.053). Comparison of SMBG and CGM results demonstrated significant difference only at one timepoint (at 1 hour after lunch) and only in group 1: median glycemia was 7.4 [6.9; 8.1] mmol/L by SMBG and 6 [5.4; 6.6] mmol/L by CGM measurement (р = 0.001). Lower median values by CGM measurement could be explained by averaging of three successive measurements carried out in the period of rapid changes of glycemia. Conclusion: The achievement of control of diabetes by hba1c doesn't necessarily reflect current achievement of the target glycemic levels. As long as there was no significant difference in glycemia measured by SMBG and CGM, we conclude that CGM doesn't have any advantage over routine frequent SMBG in pregnant women.Publication Open Access Recent advances in operations research in computational biology, bioinformatics and medicine(EDP Sciences, 2014) Felici, Giovanni; Szachniuk, Marta; Lukasiak, Piotr; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956The EURO Working Group on Operations Research in Computational Biology, Bioinformatics and Medicine held its fourth conference in Poznan-Biedrusko, Poland, June 26-28, 2014. The editorial board of RAIRO-OR invited submissions of papers to a special issue on Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine. This special issue includes nine papers that were selected among forty presentations and included in this special issue after two rounds of reviewing.Publication Open Access Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6(EDP Sciences, 2016) Department of Industrial Engineering; N/A; Department of Chemical and Biological Engineering; N/A; Tardu, Mehmet; Rahim, Fatih; Kavaklı, İbrahim Halil; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 40319; 24956Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure-based drug discovery. However, virtual screening of chemical libraries with millions of compounds requires a lot of time for computing and data analysis. A priori classification of compounds in the libraries as low-and high-binding free energy sets decreases the number of compounds for virtual screening experiments. This classification also reduces the required computational time and resources. Data analysis is demanding since a compound can be described by more than one thousand attributes that make any data analysis very challenging. In this paper, we use the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on a target protein, SIRT6. The results indicate that the proposed approach outperforms other approaches reported in the literature with 83.55% accuracy using six common molecular descriptors (SC-5, SP-6, SHBd, minHaaCH, maxwHBa, FMF). Additionally, the top 10 hit compounds are determined and reported as the candidate inhibitors of SIRT6 for which no inhibitors have so far been reported in the literature.