Uyar, Aslı
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Uyar Aslı
Aslı, Uyar
A., Uyar
Aslı Uyar
Aslı UYAR
UYAR Aslı
Uyar, A.
Uyar, Asli
Asli Uyar
Uyar Asli
Uyar A.
UYAR Asli
Asli UYAR
Uyar, Aslı
Aslı, Uyar
A., Uyar
Aslı Uyar
Aslı UYAR
UYAR Aslı
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
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
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Scholarly Output
15
Articles
9
Citation Count
298
Supervised Theses
0
15 results
Scholarly Output Search Results
Now showing 1 - 10 of 15
Article Citation Count: 48Metabolomic Assessment of Embryo Viability(Thieme Medical Publ inc, 2014) Uyar, Asli; Uyar, Aslı; Bilgisayar Mühendisliği / Computer EngineeringPreimplantation 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.Conference Object Citation Count: 0Sampling bias in microarray data analysis: A demonstration in the field of reproductive biology(IEEE Computer Society, 2013) Manafi,S.; Uyar, Aslı; Bener,A.; Bilgisayar Mühendisliği / Computer EngineeringThe actual benefit from high-throughput microarray experiments strongly relies on elimination of all possible sources of biases during both the experimental procedure and data analysis process. Within the context of reproductive biology, microarray based transcriptomic analysis of oocyte and surrounding cumulus/granulosa cells poses significant challenges due to limited amount of samples and/or potential contaminations from adjacent cells. In this study, we investigated the effect of sampling bias on consistency of the microarray differential expression analysis in the field of reproduction. Experiments were conducted on five datasets obtained from publicly available microarray repositories. For each dataset, probe level expression values were extracted and background adjustment, inter-array quantile normalization and probe set summarization were performed according to the Robust Multi-Chip Average algorithm. Genes with a false discovery rate-corrected p value of <0.05 and |Fold Change| > 2 were considered as differentially expressed. Results demonstrate that both number of replicates and including different subsets of available samples in the analysis alter the number of differentially expressed genes. We suggest that assessment of inter-sample variance prior to differential expression analysis is an important step in microarray experiments and proper handling of that variance may require alternative normalization and/or statistical test methods. © 2013 IEEE.Conference Object Citation Count: 5AN ANALYSIS OF CONCURRENCE ENTANGLEMENT MEASURE AND QUANTUM FISHER INFORMATION OF QUANTUM COMMUNICATION NETWORKS OF TWO-QUBITS(Ieee, 2014) Uyar, Aslı; Bugu, Sinan; Ozaydin, Fatih; Altintas, Azmi Ali; Bilgisayar Mühendisliği / Computer EngineeringQuantum 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.Article Citation Count: 26Non-invasive assessment of embryonic sex in cattle by metabolic fingerprinting of in vitro culture medium(Springer, 2014) Munoz, Marta; Uyar, Aslı; Correia, Eva; Diez, Carmen; Fernandez-Gonzalez, Alfonso; Nestor Caamano, Jose; Gomez, Enrique; Bilgisayar Mühendisliği / Computer EngineeringThe 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.Article Citation Count: 42Prediction of pregnancy viability in bovine in vitro-produced embryos and recipient plasma with Fourier transform infrared spectroscopy(Elsevier Science inc, 2014) Munoz, M.; Uyar, Aslı; Correia, E.; Diez, C.; Fernandez-Gonzalez, A.; Caamano, J. N.; Gomez, E.; Bilgisayar Mühendisliği / Computer EngineeringWe analyzed embryo culture medium (CM) and recipient blood plasma using Fourier transform infrared (FTIR) metabolomics to predict pregnancy outcome. Individually cultured, in vitro-produced (IVP) blastocysts were transferred to recipients as fresh and vitrified-warmed. Spent CM and plasma samples were evaluated using FTIR. The discrimination capability of the classifiers was assessed for accuracy, sensitivity (pregnancy), specificity (nonpregnancy), and area under the receiver operator characteristic curve (AUC). Within all IVP fresh embryos (birth rate = 52%), high AUC were obtained at birth, especially with expanded blastocysts (CM: 0.80 +/- 0.053; plasma: 0.89 +/- 0.034). The AUC of vitrified IVP embryos (birth rate = 31%) were 0.607 +/- 0.038 (CM, expanded blastocysts) and 0.672 +/- 0.023 (plasma, all stages). Recipient plasma generally predicted pregnancy outcome better than did embryo CM. Embryos and recipients with improved pregnancy viability were identified, which could increase the economic benefit to the breeding industry.Conference Object Citation Count: 0Whole Genome Analysis Identifies Interleukin-6 Mediated Inhibition of Interferon-Induced 15 kDa Protein (ISG15), an Auto-Inflammation Suppressor Protein in Preeclamptic Cytotrophoblasts(Sage Publications inc, 2015) Uyar, Aslı; Semerci, Nihan; Uyar, Asli; Kayisli, Ozlem Guzeloglu; Guller, Seth; Schatz, Frederick; Kayisli, Umit A.; Bilgisayar Mühendisliği / Computer Engineering[No Abstract Available]Article Citation Count: 1Yapay Zeka Tabanlı Klinik Karar Destek Sistemi ile Tüp Bebek Tedavisi Gebelik Sonucu Tahmini(2021) Uyar, Aslı; Attar, Rukset; Şengül, Yasemin Atılgan; Alagöz, Oya; Uyar, Aslı; Bilgisayar Mühendisliği / Computer EngineeringTüp 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.Article Citation Count: 61Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods(Sage Publications inc, 2015) Uyar, Asli; Uyar, Aslı; Ciray, H. Nadir; Bilgisayar Mühendisliği / Computer EngineeringBackground. 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.Conference Object Citation Count: 0PERFORMANCE ANALYSIS OF CLASSIFICATION MODELS FOR MEDICAL DIAGNOSTIC DECISION SUPPORT SYSTEMS(Ieee, 2013) Uyar, Aslı; Uyar, Asli; Bilgisayar Mühendisliği / Computer EngineeringAs 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.Review Citation Count: 49The impact of assisted reproductive technologies on genomic imprinting and imprinting disorders(Lippincott Williams & Wilkins, 2014) Uyar, Asli; Uyar, Aslı; Bilgisayar Mühendisliği / Computer EngineeringPurpose 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.
