PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - WoS: 3Citation - Scopus: 3NeRNA: A Negative Data Generation Framework for Machine Learning Applications of Noncoding RNAs(Pergamon-Elsevier Science Ltd, 2023-06) Orhan, Mehmet Emin; Demirci, Yilmaz Mehmet; Demirci, Mueserref Duygu Sacar; Saçar Demirci, Müşerref DuyguMany supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them might even have weak or strong experimental validation. On the contrary, there are neither databases listing the confirmed negative sequences for a specific ncRNA class nor standardized methodologies developed to generate high quality negative examples. To overcome this challenge, a novel negative data generation method, NeRNA (negative RNA), is developed in this work. NeRNA uses known examples of given ncRNA sequences and their calculated structures for octal representation to create negative sequences in a manner similar to frameshift mutations but without deletion or insertion. NeRNA is tested individually with four different ncRNA datasets including MicroRNA (miRNA), transfer RNA (tRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA). Furthermore, a species-specific case analysis is per-formed to demonstrate and compare the performance of NeRNA for miRNA prediction. The results of 1000 fold cross-validation on Decision Tree, Naive Bayes and Random Forest classifiers, and deep learning algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Simple feedforward Neural Networks indicate that models obtained by using NeRNA generated datasets, achieves substantially high prediction performance. NeRNA is released as an easy-to-use, updatable and modifiable KNIME workflow that can be downloaded with example datasets and required extensions. In particular, NeRNA is designed to be a powerful tool for RNA sequence data analysis.Book Part Citation - Scopus: 4Computational Detection of Pre-MicroRNAs(Humana Press Inc., 2021-08-26) Saçar Demirci, Müşerref DuyguMicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed. © 2021 Elsevier B.V., All rights reserved.Article Citation - WoS: 24Citation - Scopus: 24Circular RNA-MicroRNA Interaction Predictions in SARS-CoV Infection(Walter de Gruyter Gmbh, 2021-03-01) Demirci, Yilmaz Mehmet; Demirci, Muserref Duygu Sacar; Saçar Demirci, Müşerref DuyguDifferent types of noncoding RNAs like MicroRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis work flow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.