Uyar, Aslı

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Uyar Aslı
Aslı, Uyar
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Aslı Uyar
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Uyar, A.
Uyar, Asli
Asli Uyar
Uyar Asli
Uyar A.
UYAR Asli
Asli UYAR
Uyar, Aslı
Job Title
Dr.Öğr.Üyesi
Email Address
asli.uyar@okan.edu.tr
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Bilgisayar Mühendisliği / Computer Engineering
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Scholarly Output

15

Articles

9

Citation Count

298

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0

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2013 - 2019122020 - 20213
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Now showing 1 - 10 of 15
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    Article
    Citation - WoS: 44
    Citation - Scopus: 48
    Poor ovarian response in women undergoing in vitro fertilization is associated with altered microRNA expression in cumulus cells
    (Elsevier Science inc, 2015) Karakaya, Cengiz; Guzeloglu-Kayisli, Ozlem; Uyar, Asli; Kallen, Amanda N.; Babayev, Elnur; Bozkurt, Nuray; Seli, Emre; Bilgisayar Mühendisliği / Computer Engineering
    Objective: To analyze the association of micro-ribonucleic acid (miRNA) expression with the number of oocytes retrieved, in women undergoing in vitro fertilization (IVF). Design: Experimental study. Setting: Academic medical center. Patient(s): A total of 189 women undergoing IVF-intracytoplasmic sperm injection (ICSI). Intervention(s): Pooled cumulus cells were collected. Main Outcome Measure(s): Poor responders were identified as patients who produced fewer oocytes than the 25th percentile of their respective age group. MicroRNAs were extracted from cumulus cells, and an miRNA microarray was performed, comparing poor responders (n = 3) to non-poor responders (n = 3). Expression of miR-21-5p (active strand of miR-21) and miR-21-3p was tested in poor responders (n - 21) and non-poor responders (n = 29), using reverse transcription real-time polymerase chain reaction (qRT-PCR). Regulation of miR-21-5p and miR-21-3p, in human granulosa-like tumor (KGN) cells, by estradiol (E-2), was tested in vitro. Result(s): MicroRNA microarray analysis showed up-regulation of 16 miRNAs and down-regulation of 88 miRNAs in poor responders. Notably, miR-21 was significantly up-regulated 5-fold in poor-responder samples. Analysis using qRT-PCR confirmed that miR-21-5p expression was significantly up-regulated in poor responders, whereas miR-21-3p expression was significantly lower, suggesting that elevated miR-21-5p expression in cumulus cells is not regulated at the pre-miR-21 level in poor responders. Both miR-21-5p and miR-21-3p were increased in KGN cells in response to higher doses of E2; their expression was not affected at lower E2 concentrations. Conclusion(s): We found that poor response to IVF is associated miR-21-5p, and that this elevated expression is independent of lower serum E2 levels in poor responders. (C) 2015 by American Society for Reproductive Medicine.
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    Conference Object
    Citation - WoS: 0
    PERFORMANCE ANALYSIS OF CLASSIFICATION MODELS FOR MEDICAL DIAGNOSTIC DECISION SUPPORT SYSTEMS
    (Ieee, 2013) Segmen, Esref; Uyar, Asli; Bilgisayar Mühendisliği / Computer Engineering
    As a part of electronic healthcare systems, medical diagnostic decision support systems have been more popular in clinical routine. It is critical to decide the best model to provide reliable machine learning based decision support in diagnostic problems. In this study, the performance of common classification algorithms have been comparatively evaluated using public medical datasets. The experimental results reveal that, although there is no single best algorithm for all datasets, MLP and Naive Bayes methods have provided relatively higher success rates.
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    Review
    Citation - WoS: 45
    Citation - Scopus: 50
    The impact of assisted reproductive technologies on genomic imprinting and imprinting disorders
    (Lippincott Williams & Wilkins, 2014) Uyar, Asli; Seli, Emre; Bilgisayar Mühendisliği / Computer Engineering
    Purpose of reviewGenomic imprinting refers to preferential allele-specific gene expression. DNA methylation-based molecular mechanisms regulate establishment and maintenance of parental imprints during early embryo development and gametogenesis. Because of the coincident timing, a potential association between assisted reproductive technology (ART) procedures and imprinting defects has been investigated in various studies. In this review, we provide an overview of genomic imprinting and present a summary of the relevant clinical data.Recent findingsART procedures affect DNA methylation pattern, parental imprinting status, and imprinted gene expression in the mouse embryo. In humans, several case series suggested an association between ART and imprinting disorders, with a three-fold to six-fold higher prevalence of ART use among children born with Beckwith-Wiedemann syndrome compared to the general population. However, more recent studies failed to support these findings and could not demonstrate an association between imprinting disorders and ARTs, independent of subfertility.SummaryART procedures may affect methylation status of imprinted regions in the DNA, leading to imprinting disorders. Although the low prevalence of imprinting disorders makes it challenging to perform conclusive clinical trials, further studies in large registries are required to determine the real impact of ARTs on their occurrence.
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    Article
    Citation - WoS: 60
    Citation - Scopus: 73
    Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods
    (Sage Publications inc, 2015) Uyar, Asli; Bener, Ayse; Ciray, H. Nadir; Bilgisayar Mühendisliği / Computer Engineering
    Background. Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. Objective. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Design. Retrospective cohort study. Data Source. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. Methods. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. Results. The naive Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). Conclusions. A machine learning-based decision support system would be useful in improving the success rates of IVF treatment.
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    Conference Object
    Citation - Scopus: 2
    Performance analysis of classification models for medical diagnostic decision support systems;
    (2013) Segmen,E.; Uyar,A.; Bilgisayar Mühendisliği / Computer Engineering
    As a part of electronic healthcare systems, medical diagnostic decision support systems have been more popular in clinical routine. It is critical to decide the best model to provide reliable machine learning based decision support in diagnostic problems. In this study, the performance of common classification algorithms have been comparatively evaluated using public medical datasets. The experimental results reveal that, although there is no single best algorithm for all datasets, MLP and Naive Bayes methods have provided relatively higher success rates. © 2013 IEEE.
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    Article
    Yapay Zeka Tabanlı Klinik Karar Destek Sistemi ile Tüp Bebek Tedavisi Gebelik Sonucu Tahmini
    (2021) Pacci, Zeynep; Attar, Rukset; Şengül, Yasemin Atılgan; Alagöz, Oya; Uyar, Aslı; Bilgisayar Mühendisliği / Computer Engineering
    Tüp bebek tedavisi başarı olasılığının henüz tedavi başlamadan belirlenmesi hastalar ve klinisyenler açısından önem taşımaktadır. Yapay zeka tabanlı klinik karar destek sistemleri, geçmiş tedavi verilerini analiz ederek yeni tedavilerde gebelik sonucunun tahmin edilmesine olanak sağlar. Bu çalışmada, tüp bebek tedavisine başlayacak hastaya ait öznitelikler kullanılarak pozitif gebelik olasılığını hesaplayan bir model geliştirilmiştir. Çalışmada kullanılan veri kümesi Yeditepe Üniversitesi Hastanesi Tüp Bebek Kliniği’nde 2013-2019 yılları arasında gerçekleştirilen 1154 adet tedavi siklusuna ait elektronik sağlık kayıtlarından oluşmaktadır. Veri kümesi üzerinde beş farklı sınıflandırma yöntemi (Destek Vektör Makineleri, Çok Katmanlı Algılayıcı, Rastgele Orman, Aşırı Gradyan Artırma ve Hafif Gradyan Artırma) 5-katlı çapraz doğrulama yöntemi kullanılarak karşılaştırmalı olarak incelenmiştir. Gebelik sonucu tahmininde en yüksek sınıflandırma performansı Destek Vektör Makineleri yöntemi ile elde edilmiş (AUC=0.70) ve sınıflandırma olasılık sonuçlarında karar eşik değerinin optimizasyonu ile tahmin doğruluğu daha da iyileştirilerek gebelik sonucunun %71.7 Doğru Pozitif ve %59.4 Doğru Negatif oranı ile tahmin edilmesi sağlanmıştır.
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    Article
    Citation - WoS: 45
    Citation - Scopus: 50
    Metabolomic Assessment of Embryo Viability
    (Thieme Medical Publ inc, 2014) Uyar, Asli; Seli, Emre; Bilgisayar Mühendisliği / Computer Engineering
    Preimplantation embryo metabolism demonstrates distinctive characteristics associated with the developmental potential of embryos. On this basis, metabolite content of culture media was hypothesized to reflect the implantation potential of individual embryos. This hypothesis was tested in consecutive studies reporting a significant association between culture media metabolites and embryo development or clinical pregnancy. The need for a noninvasive, reliable, and rapid embryo assessment strategy promoted metabolomics studies in vitro fertilization (IVF) in an effort to increase success rates of single embryo transfers. With the advance of analytical techniques and bioinformatics, commercial instruments were developed to predict embryo viability using spectroscopic analysis of surplus culture media. However, despite the initial promising results from proof-of-principal studies, recent randomized controlled trials using commercial instruments failed to show a consistent benefit in improving pregnancy rates when metabolomics is used as an adjunct to morphology. At present, the application of metabolomics technology in clinical IVF laboratory requires the elimination of factors underlying inconsistent findings, when possible, and development of reliable predictive models accounting for all possible sources of bias throughout the embryo selection process.
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    Article
    Citation - Scopus: 0
    Rejection threshold optimization using 3D ROC curves: Novel findings on biomedical datasets
    (Ismail Saritas, 2021) Uyar,A.; Sengul,Y.A.; Bilgisayar Mühendisliği / Computer Engineering
    Reject option is introduced in classification tasks to prevent potential misclassifications. Although optimization of error-reject trade-off has been widely investigated, it is shown that error rate itself is not an appropriate performance measure, when misclassification costs are unequal or class distributions are imbalanced. ROC analysis is proposed as an alternative approach to performance evaluation in terms of true positives (TP) and false positives (FP). Considering classification with reject option, we need to represent the tradeoff between TP, FP and rejection rates. In this paper, we propose 3D ROC analysis to determine the optimal rejection threshold as an analogy to decision threshold optimization in 2D ROC curves. We have demonstrated our proposed method with Naive Bayes classifier on Heart Disease dataset and validated the efficiency of the method on multiple datasets from UCI Machine Learning Repository. Our experiments reveal that classification with optimized rejection threshold significantly improves true positive rates in biomedical datasets. Furthermore, false positive rates remain the same with rejection rates below 10% on average. © 2021, Ismail Saritas. All rights reserved.
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    Conference Object
    Citation - WoS: 5
    AN ANALYSIS OF CONCURRENCE ENTANGLEMENT MEASURE AND QUANTUM FISHER INFORMATION OF QUANTUM COMMUNICATION NETWORKS OF TWO-QUBITS
    (Ieee, 2014) Erol, Volkan; Bugu, Sinan; Ozaydin, Fatih; Altintas, Azmi Ali; Bilgisayar Mühendisliği / Computer Engineering
    Quantum entanglement is one of key concepts in quantum communication engineering. Ordering the quantum systems according to their entanglement measures is a popular problem of the field. For two level (qubit) systems of two particles, state ordering has been studied with respect to well-known entanglement measures such as Concurrence, Negativity and Relative Entropy of Entanglement (REE) [1-5]. In this work, we study the state ordering of the two-qubit systems with respect to Quantum Fisher Information vs. Concurrence. In particular, constructing 1K random states and calculating their Concurrences and Negativities, we obtain the orderings of the states by comparing these results with Quantum Fisher Information values and present our results which are interesting when compared to that of two-level systems.
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    Article
    Citation - WoS: 26
    Citation - Scopus: 28
    Non-invasive assessment of embryonic sex in cattle by metabolic fingerprinting of in vitro culture medium
    (Springer, 2014) Munoz, Marta; Uyar, Asli; Correia, Eva; Diez, Carmen; Fernandez-Gonzalez, Alfonso; Nestor Caamano, Jose; Gomez, Enrique; Bilgisayar Mühendisliği / Computer Engineering
    The objective of this work was to determine whether metabolic fingerprinting of spent bovine embryo culture media using Fourier transform infrared spectroscopy (FTIR) correlates with embryonic sex. Embryos were produced in vitro from oocytes collected from cows slaughtered in an abattoir. Day-6 embryos were individually cultured in synthetic oviduct fluid for 24 h, prior to the time (Day-7) intended for embryo transfer or cryopreservation. Culture medium was analyzed by FTIR. Embryos were sexed by a PCR procedure based on amelogenin gene amplification or transferred to a recipient and sex observed at birth. Media samples from embryos diagnosed as male (n = 47) or female (n = 70) were individually collected and evaluated using FTIR. The spectra obtained were analyzed according to metabolomic profile of embryo culture media and embryonic sex. The discrimination capability of the classifiers was assessed for accuracy, sensitivity (female), sensitivity (male) and area under the ROC curve (AUC). Performance of sex prediction (%) was high within early blastocysts + blastocysts (74.4 +/- A 10.2, accuracy; 0.749 +/- A 0.099, AUC) and excellent for expanded blastocysts (86.0 +/- A 12.6, accuracy; 0.898 +/- A 0.094, AUC). A combination of metabolomic and bioinformatic analysis provides a non-invasive mean of embryonic sex analysis.