TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Research Project RNA İkincil Yapılarının Çok Boyutlu Gösterimi ve Pre-MiRNA Tespiti İçin Uygulamaları(2021) Demirci, Müşerref Duygu Saçar; Demirci, Yılmaz Mehmet-Article Citation - WoS: 3Citation - Scopus: 3MicroRNA Prediction Based on 3D Graphical Representation of RNA Secondary Structures(Tubitak Scientific & Technological Research Council Turkey, 2019-08-05) Sacar Demirci, Muserref Duygu; Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naive Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.Article Evaluation of Sub-Network Search Programs in Epilepsy-Related GWAS Dataset(Pamukkale Univ, 2022) Adanur Dedeturk, Beyhan; Bakir Gungor, Burcu; Dedeturk, Beyhan Adanur; Gungor, Burcu BakirThe active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network. In recent years, several algorithms have been developed for this problem. In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set. Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown. The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information. Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified. In addition, running times and features of the programs are comparatively evaluated.Article Control of Collective Bursting in Small Hodgkinhuxley Neuron Clusters(2018) Borisenok, Sergey; Catmabacak, Onder; Şenel, ZeynepThe speed gradient-based control algorithm for tracking the membranepotential of Hodgkin-Huxley neurons is applied to their small clusters modeling thebasic features of an epileptiform dynamics. One of the neurons plays a role of controlelement detecting the temporal hyper-synchronization among its network companionsand switching their bursting behavior to resting. The ‘toy’ model proposed in thepaper can serve as an algorithmic basement for developing special control elements atthe scale of one or few cells that may work autonomously and are able to detect andsuppress epileptic behavior in the networks of real biological neurons.Article Computational Identification of MicroRNAs From Ssdna Viruses(2018-09-30) Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and the fact that they are associated with variousdisease phenotypes is one of the main reasons for their importance. The complexity of experimental detection of miRNAs dueto their characteristics led to the development of computational methods. In this work, a machine learning based approach wasapplied to identify and analyze potential miRNAs that might be originated from 60 single strand DNA (ssDNA) viruses’genomes. The results suggest that 53 of these viruses may possibly produce proper miRNA precursors. Moreover, thepossibility of these candidate miRNA precursors’ ability to generate mature miRNAs that could target human genes and viralgenomes has been tested. Overall, the outcomes of this research indicate that there might be another level of host-virusinteraction through miRNAs which requires further experimental confirmation.