Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Classification of Patients with Heart Failure(2014) Bayrak, Tuncay; Ogul, Hasan; https://orcid.org/0000-0001-6826-4350; U-4603-2019Echocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.Item Computational Prediction of MicroRNA Function and Activity(2014) Ogul, Hasan; 24272442Inferring microRNA (miRNA) functions and activities has been extremely important to understand their system-level roles and the mechanisms behind the cellular behaviors of their target genes. This chapter first details methodologies necessary for prediction of function and activity. It then introduces the computational methods available for investigation of sequence and experimental data and for analysis of the information flow mediated through miRNAs.Item Predicting microRNA Expression from Sequence(2015) Ogul, Hasan; Tuncer, M. EmreGiven the promoter sequence of a microRNA, we attempt to predict its expression using a regression model learnt from the expression levels of other microRNAs obtained through a microarray experiment. To our knowledge, this is the first study that evaluates the predictability of microRNA expression from sequence. The promising results encourage the use of the system as a supporting means for microarray missing data imputation or completing old experiments with new explorations.Item Retrieving Relevant Experiments: The Case of MicroRNA Microarrays(2015) Acici, Koray; Terzi, Yunus Kasim; Ogul, Hasan; 0000-0001-5612-9696; 0000-0002-3821-6419; 26116091; B-4372-2018; HDM-9910-2022Content-based retrieval of biological experiments in large public repositories is a recent challenge in computational biology and bioinformatics. The task is, in general, to search in a database using a query-by-example without any experimental meta-data annotation. Here, we consider a more specific problem that seeks a solution for retrieving relevant microRNA experiments from microarray repositories. A computational framework is proposed with this objective. The framework adapts a normal-uniform mixture model for identifying differentially expressed microRNAs in microanay profiling experiments. A rank-based thresholding scheme is offered to binarize real-valued experiment fingerprints based on differential expression. An effective similarity metric is introduced to compare categorical fingerprints, which in turn infers the relevance between two experiments. Two different views of experimental relevance are evaluated, one for disease association and another for embryonic germ layer, to discern the retrieval ability of the proposed model. To the best of our knowledge, the experiment retrieval task is investigated for the first time in the context of microRNA microarrays. (C) 2015 Elsevier Ireland Ltd. All rights reserved.Item miSEA: MicroRNA Set Enrichment Analysis(2015) Corapcioglu, M. Erdem; Ogul, Hasan; 26093049We introduce a novel web-based tool, miSEA, for evaluating the enrichment of relevant microRNA sets from microarray and miRNA-Seq experiments on paired samples, e.g. control vs. treatment. In addition to a group of previously annotated microRNA sets embedded in the system, this tool enables users to import new microRNA sets obtained from their own research. miSEA allows users to select from a large variety of microRNA grouping categories, such as family classification, disease association, common regulation, and genome coordinates, based on their requirements. miSEA therefore provides a knowledge-driven representation scheme for microRNA experiments. The usability of this platform was discerned with a cancer type-classification task performed on a set of real microRNA expression profiling experiments. The miSEA web server is available at http://www.baskent.edu.tr/similar to hogul/misea (C) 2015 Elsevier Ireland Ltd. All rights reserved.Item Inferring Similarity between Time-Series Microarrays: A Content-based Approach(2015) Sener, Duygu Dede; Ogul, Hasan; 0000-0001-6766-4977Public repositories for gene expression studies have been growing rapidly in the last decade. Retrieval of gene expression experiments based on textual descriptions does not provide sufficient data for biologists and clinicians. Content-based search has recently become more desirable in retrieving similar experiments. Current methods for content-based retrieval cannot address the problem of profiling the gene behaviors in multiple measurement points, i.e. in time course. This study, to the best of our knowledge, is the first attempt to build a fingerprint for each gene by considering all time points to infer its time-course profile to represent the experiment content in an information retrieval framework. An empirical study is performed on a large dataset of Arabidopsis microarrays from Gene Expression Omnibus (GEO). Experimental results show that relevant experiments are retrieved based on content similarity.Item Evaluating Text Features for Lyrics-Based Songwriter Prediction(2015) Kirmaci, Basar; Ogul, Hasan; 978-1-4673-7939-7We offer an automated way of estimating the author of a song using only its lyrics content. To this end, we introduce a complete text classification framework which takes raw lyrics data as input and report estimated songwriter. The performance of the system is evaluated based on its classification and retrieval ability on a large dataset of Turkish songs, which was collected in this study. The results promote the use of such technique as a complementary tool in music information retrieval applications.Item Inferring Microarray Relevance By Enrichment Of Chemotherapy Resistance-Based MicroRNA Sets(2015) Acici, Koray; Ogul, Hasan; 0000-0002-3821-6419; HDM-9910-2022Inferring relevance between microarray experiments stored in a gene expression repository is a helpful practice for biological data mining and information retrieval studies. In this study, we propose a knowledge-based approach for representing microarray experiment content to be used in such studies. The representation scheme is specifically designed for inferring a disease-associated relevance of microRNA experiments. A group of annotated microRNA sets based on their chemotherapy resistance are used for a statistical enrichment analysis over observed expression data. A query experiment is then represented by a single dimensional vector of these enrichment statistics, instead of raw expression data. According to the results, new representation scheme can provide a better retrieval performance than traditional differential expression-based representation.Item Author Recognition from Lyrics(2015) Kirmaci, Basar; Ogul, HasanMusic information retrieval has been an important task due to the wide use of internet and related technologies for entertainment. In previous studies, the problem has been considered using the meta-data or melodic content. The use of lyrics in this context is not that common. There is not study either for Turkish songs in this respect. In this study, we discuss the predictability of the author using the text data in a Turkish lyric. To this end, we propose a system that can predict the author using the features extracted from text content. The performance of the system is evaluated on a large data set collected from writers with different music styles.Item Comparison of Similarity Metrics in Microarray Experiment Retrieval(2015) Acici, Koray; Ogul, Hasan; 0000-0002-3821-6419; HDM-9910-2022Content-based retrieval of biological experiments is a recent challenge in bioinformatics. The task is to search in a database using a query-by-example without any meta-data annotation. In this study, for retrieving relevant microRNA experiments from microarray repositories, performance evaluation of known similarity metrics was conducted to compare experiment fingerprints. It was shown that Spearman correlation coefficient outperformed others by comparison on real datasets. This result shows that ranks of fingerprint values are more important than the exact values in experiment fingerprint.