Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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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 Text-based Experiment Retrieval in Genomic Databases(2022) Sener, Duygu Dede; Ogul, Hasan; Basak, Selen; https://orcid.org/0000-0001-6766-4977With the growing number of genomic data in public repositories, efficient search methodologies have become a basic need to reach the relevant genomic data. However, this need cannot be fulfilled with the current repositories because they offer a limited search option which is a lexical matching of textual descriptions or metadata of the experiments. This technique is insufficient to get the required information needed to detect similarities between experiments within a large data collection. Due to the limitation of the existing repositories, in this study, we develop a text-based experiment retrieval framework by using both lexical and semantic similarity approaches to find similarities between experiments, and their retrieval performance was compared. This study is the first attempt to use text-driven semantic analysis approaches for developing a retrieval framework for experiments. An empirical study was conducted on a large textual description of Arabidopsis microarray experiments from the Gene Expression Omnibus database. In the proposed model, Jaccard similarity was used as a lexical similarity approach; Latent Semantic Analysis, Probabilistic Latent Semantic Analysis and Latent Dirichlet allocation were used as semantic similarity approaches to detect similarities between the textual descriptions of the experiments. According to the experimental results, relevant experiments can be retrieved successfully by text-driven semantic similarity approaches compared with the lexical similarity approach.Item Multi Level Lecture Video Classification Using Text Content(2020) Agziyagli, Veysel Sercan; Ogul, HasanRecent interest in e-learning and distance education services has significantly increased the amount of lecture video data in public and institutional repositories. In their current forms, users can browse in these collections using meta-data-based search queries such as course name, description, instructor and syllabus. However, lecture video entries have rich contents, including image, text and speech, which can not be easily represented by meta-data annotations. Therefore, there is an emerging need to develop tools that will automatically annotate lecture videos to facilitate more targeted search. A simple way to realize this is to classify lectures into known categories. With this objective, this paper presents a method for classifying videos based on extracted text content in several semantic levels. The method is based on Bidirectional Long-Short Term Memory (Bi-LSTM) applied on word embedding vectors of text content extracted by Optical Character Recognition (OCR). This approach can outperform conventional machine learning models and provide a useful solution for automatic lecture video annotation to support online education.Item mirLSTM: A Deep Sequential Approach to MicroRNA Target Binding Site Prediction(2019) Paker, Ahmet; Ogul, Hasan; HJH-2307-2023MicroRNAs (miRNAs) are small and non-coding RNAs of similar to 21-23 base length, which play critical role in gene expression. They bind the target mRNAs in the post-transcriptional level and cause translational inhibition or mRNA cleavage. Quick and effective detection of the binding sites of miRNAs is a major problem in bioinformatics. In this study, a deep learning approach based on Long Short Term Memory (LSTM) is developed with the help of an existing duplex sequence model. Compared with four conventional machine learning methods, the proposed LSTM model performs better in terms of the accuracy (ACC), sensitivity, specificity, AUC (Area under the curve) and F1 score. A web-tool is also developed to identify and display the microRNA target sites effectively and quickly.