Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 3Citation - Scopus: 5Deterioration of Pre-War and Rehabilitation of Post-War Urbanscapes Using Generative Adversarial Networks(SAGE Publications, 2023) Çiçek, Selen; Turhan, Gözde Damla; Taşer, AybükeThe urban built environment of contemporary cities confronts a constant risk of deterioration due to natural or artificial reasons. Especially political aggression and war conflicts have significant destructive effects on architectural and cultural heritage buildings. The post-war urbanscapes demonstrate the striking effects of the armed conflicts during the hot war encounters. However, the residues of the urbanscapes become the actual indicators of damage and loss. Since today we can make future predictions using a variety of machine learning algorithms, it is possible to represent hybrid projections of urban heterotopias. In this context, this research proposes to explore dystopian post-war projections for modern cities based on their architectural styles and demonstrate the utopian scenarios of rehabilitation possibilities for the damaged urban built environment of post-war cities by using generative adversarial network (GAN) algorithms. Two primary datasets containing the post-war and pre-war building facades have been given as the input data for the CycleGAN and pix2pix GAN models. Thus, two different image-to-image GAN models have been compared regarding their ability to produce legible building facade projections in architectural features. Besides, the machine learning process results have been discussed in terms of cities' utopian and dystopian future predictions, demonstrating the war conflicts' immense effects on the built environment. Moreover, the immediate consequence of the destructive aggression on tangible and intangible architectural heritage would become visible to inhabitants and policymakers when the AI-generated rehabilitation potentials have been exposed.Article Citation - WoS: 3Citation - Scopus: 4Quasi-Supervised Strategies for Compound-Protein Interaction Prediction [article](Wiley-VCH Verlag, 2021) Çakı, Onur; Karaçalı, BilgeIn-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations.Article Citation - WoS: 4Citation - Scopus: 4Predicting the Soft Error Vulnerability of Parallel Applications Using Machine Learning(Springer, 2021) Öz, Işıl; Arslan, SanemWith the widespread use of the multicore systems having smaller transistor sizes, soft errors become an important issue for parallel program execution. Fault injection is a prevalent method to quantify the soft error rates of the applications. However, it is very time consuming to perform detailed fault injection experiments. Therefore, prediction-based techniques have been proposed to evaluate the soft error vulnerability in a faster way. In this work, we present a soft error vulnerability prediction approach for parallel applications using machine learning algorithms. We define a set of features including thread communication, data sharing, parallel programming, and performance characteristics; and train our models based on three ML algorithms. This study uses the parallel programming features, as well as the combination of all features for the first time in vulnerability prediction of parallel programs. We propose two models for the soft error vulnerability prediction: (1) A regression model with rigorous feature selection analysis that estimates correct execution rates, (2) A novel classification model that predicts the vulnerability level of the target programs. We get maximum prediction accuracy rate of 73.2% for the regression-based model, and achieve 89% F-score for our classification model.Article Citation - WoS: 5Citation - Scopus: 7Fast Texture Classification of Denoised Sar Image Patches Using Glcm on Spark(Türkiye Klinikleri Journal of Medical Sciences, 2020) Özcan, Caner; Ersoy, Okan; Oğul, İskender ÜlgenClassification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library. The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results, and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method. TerraSAR-X images of high-resolution real-world SAR images were used as data.Article Citation - WoS: 11Citation - Scopus: 14Categorization of Species Based on Their Micrornas Employing Sequence Motifs, Information-Theoretic Sequence Feature Extraction, and K-Mers(Springer Verlag, 2017) Yousef, Malik; Nigatu, Dawit; Levy, Dalit; Allmer, Jens; Henkel, WernerBackground: Diseases like cancer can manifest themselves through changes in protein abundance, and microRNAs (miRNAs) play a key role in the modulation of protein quantity. MicroRNAs are used throughout all kingdoms and have been shown to be exploited by viruses to modulate their host environment. Since the experimental detection of miRNAs is difficult, computational methods have been developed. Many such tools employ machine learning for pre-miRNA detection, and many features for miRNA parameterization have been proposed. To train machine learning models, negative data is of importance yet hard to come by; therefore, we recently started to employ pre-miRNAs from one species as positive data versus another species’ pre-miRNAs as negative examples based on sequence motifs and k-mers. Here, we introduce the additional usage of information-theoretic (IT) features. Results: Pre-miRNAs from one species were used as positive and another species’ pre-miRNAs as negative training data for machine learning. The categorization capability of IT and k-mer features was investigated. Both feature sets and their combinations yielded a very high accuracy, which is as good as the previously suggested sequence motif and k-mer based method. However, for obtaining a high performance, a sufficiently large phylogenetic distance between the species and sufficiently high number of pre-miRNAs in the training set is required. To examine the contribution of the IT and k-mer features, an information gain-based feature ranking was performed. Although the top 3 are IT features, 80% of the top 100 features are k-mers. The comparison of all three individual approaches (motifs, IT, and k-mers) shows that the distinction of species based on their pre-miRNAs k-mers are sufficient. Conclusions: IT sequence feature extraction enables the distinction among species and is less computationally expensive than motif calculations. However, since IT features need larger amounts of data to have enough statistics for producing highly accurate results, future categorization into species can be effectively done using k-mers only. The biological reasoning for this is the existence of a codon bias between species which can, at least, be observed in exonic miRNAs. Future work in this direction will be the ab initio detection of pre-miRNA. In addition, prediction of pre-miRNA from RNA-seq can be done.Article Citation - WoS: 20Citation - Scopus: 25Microrna Categorization Using Sequence Motifs and K-Mers(BioMed Central Ltd., 2017) Yousef, Malik; Khalifa, Waleed; Acar, İlhan Erkin; Allmer, JensBackground: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species. Results: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values. Conclusions: We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.