Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik

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

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COAR Access Rights: search.filters.coarAccessRights.open accessSubject: search.filters.subject.Plant genetics

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  • Book
    Structural Defense of Plants and Pathogenesis
    (01. Izmir Institute of Technology, 2023) Ekinci, Berkay; Ekinci, Berkay
    The major challenge in today s world is ensuring an adequate food supply for the growing global population. Achieving this goal requires the development of crops by using sustainable agricultural strategies in ecologically suitable areas (Jan et al. 2011; Doğanlar et al. 2023). However, plant diseases, which are caused by various pathogens such as bacteria, fungi, viruses, nematodes, and herbivores, pose a significant threat to crop quality and yield. Fortunately, plants have several preexisting and induced defense and immune mechanisms to protect themselves against biotic and abiotic stresses (Jones et al. 2006; Freeman et al. 2008). This review aims to provide information on phytopathogens, the steps of pathogenesis, plants’ pre-existing structural defense mechanisms against pathogenesis, and more. It is aimed at broadening the reader's knowledge and perspective by providing a wide range of examples, from simple to complex. I hope that this review will be a good start for enlightening and inspiring all curious scientists who, like me, are enthusiastic about this field.
  • Article
    Citation - WoS: 14
    Citation - Scopus: 12
    The Impact of Feature Selection on One and Two-Class Classification Performance for Plant Micrornas
    (PeerJ Inc., 2016) Khalifa, Waleed; Yousef, Malik; Saçar Demirci, Müşerref Duygu; Allmer, Jens
    MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ~29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ~13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.