Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik

Permanent URI for this collectionhttps://hdl.handle.net/11147/9

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  • Article
    Citation - Scopus: 19
    Feature Selection Has a Large Impact on One-Class Classification Accuracy for Micrornas in Plants
    (Hindawi Publishing Corporation, 2016) Yousef, Malik; Demirci, Müşerref Duygu Saçar; Khalifa, Waleed; Allmer, Jens
    MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of 95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.
  • Conference Object
    Citation - Scopus: 19
    Data Mining for Microrna Gene Prediction: on the Impact of Class Imbalance and Feature Number for Microrna Gene Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2013) Saçar, Müşerref Duygu; Allmer, Jens
    MicroRNAs (miRNAs) are small, non-coding RNAs which are involved in the posttranscriptional modulation of gene expression. Their short (18-24) single stranded mature sequences are involved in targeting specific genes. It turns out that experimental methods are limited and that it is difficult, if not impossible, to establish all miRNAs and their targets experimentally. Therefore, many tools for the prediction of miRNA genes and miRNA targets have been proposed. Most of these tools are based on machine learning methods and within that area mostly two-class classification is employed. Unfortunately, truly negative data is impossible to attain and only approximations of negative data are currently available. Also, we recently showed that the available positive data is not flawless. Here we investigate the impact of class imbalance on the learner accuracy and find that there is a difference of up to 50% between the best and worst precision and recall values. In addition, we looked at increasing number of features and found a curve maximizing at 0.97 recall and 0.91 precision with quickly decaying performance after inclusion of more than 100 features. © 2013 IEEE.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 8
    Mir-17 in Imatinib Resistance and Response To Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia Cells
    (Zerbinis Medical Publications, 2013) Fıratlıgil, Burcu; Biray Avcı, Çığır; Baran, Yusuf
    In this study we examined the expression levels of miR-17 which possesses oncogenic activities through downregulation of CDKN1A, p21 and E2F1 tumor suppressor genes, in imatinib sensitive and resistant chronic myeloid leukemia (CML) cells. On the other hand, we also determined the expression levels of miR-17 in response to tyrosine kinase inhibitors imatinib, nilotinib and dasatinib used for the treatment of CML. Methods: The expression profiles of miR-17 were analysed by Stem-Loop reverse transcription (RT) polymerase chain reaction (PCR). Results: The results revealed significant increase in the expression levels of miR-17 in imatinib sensitive and resistant cells compared to peripheral blood mononuclear cells (PBMCs). On the other hand, significant decrease was observed in miR-17 levels in response to imatinib, nilotinib and dasatinib. Conclusion: These results may imply that miR-17 can be used for diagnosis and treatment of CML.