Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Ogul, HasanSubject: search.filters.subject.microRNA

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Now showing 1 - 5 of 5
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    Inferring Microarray Relevance By Enrichment Of Chemotherapy Resistance-Based MicroRNA Sets
    (2015) Acici, Koray; Ogul, Hasan; 0000-0002-3821-6419; HDM-9910-2022
    Inferring 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.
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    Comparison of Similarity Metrics in Microarray Experiment Retrieval
    (2015) Acici, Koray; Ogul, Hasan; 0000-0002-3821-6419; HDM-9910-2022
    Content-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.
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    Exploiting Active MicroRNA Interactions for Diagnosis from Expression Profiling Experiments
    (2017) Corapcioglu, Erdem; Ogul, Hasan
    In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a model learned from previously labeled samples. While several powerful machine learning models exist to perform this task, the challenging issue is how to feed these models by effectively encoded samples. This encoding requires a sensible representation of experiment content. In contrast to previous data-driven representations based on observed differential expressions of individual miRNAs, we offer here a interaction-driven representation scheme that considers the active interactions of miRNAs with other entities in either direction of gene regulation, i.e. regulating or being regulated. We use the enrichment scores of the miRNA sets annotated by corresponding interaction elements to encode the input experiments. We have empirically shown that the new encoding can lead to a higher disease classification accuracy compared with the traditional data-driven approach.
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    Sequence Analysis to Predict Microrna Chemotherapy Resistance
    (2016) Igdeli, Muratcan; Yilmaz, Atif; Ogul, Hasan
    Recent findings suggest that microRNAs play important role in resistance to certain chemotherapies. The knowledge of what microRNAs are potentially resistant to given chemotherapies is therefore a crucial knowledge on drug design and therapy scheduling activities. In this study, we attempt to predict the list of microRNAs which are resistant to given drug using solely their mature sequence information. With this objective, we employ three common approaches for sequence classification in bioinformatics, i.e. pairwise, generative and discriminative models. The experimental results on a knowledge-driven dataset promote the use of pairwise models as a complementary tool in association studies for microRNAs and drugs.
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    Exploiting Active Microrna Interactions for Diagnosis from Expression Profiling Experiments
    (2017) Corapcioglu, Erdem; Ogul, Hasan
    In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a model learned from previously labeled samples. While several powerful machine learning models exist to perform this task, the challenging issue is how to feed these models by effectively encoded samples. This encoding requires a sensible representation of experiment content. In contrast to previous data-driven representations based on observed differential expressions of individual miRNAs, we offer here a interaction-driven representation scheme that considers the active interactions of miRNAs with other entities in either direction of gene regulation, i.e. regulating or being regulated. We use the enrichment scores of the miRNA sets annotated by corresponding interaction elements to encode the input experiments. We have empirically shown that the new encoding can lead to a higher disease classification accuracy compared with the traditional data-driven approach.