Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
Browse
3 results
Search Results
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 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 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.