Adapting And Enhancing Hybrid Computational Methods For RNA Secondary Structure Prediction

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dc.contributor.authoral-Khatib, Ra'ed Mohammad Ali
dc.date.accessioned2018-12-26T06:35:03Z
dc.date.available2018-12-26T06:35:03Z
dc.date.issued2011-12
dc.description.abstractThe secondary structure of RNA with pseudoknots is widely utilized for tracing the RNA tertiary structure, which is a key to understanding the functions of the RNAs and their useful roles in developing drugs for viral diseases. Experimental methods for determining RNA tertiary structure are time consuming and tedious. Therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. This thesis presents a hybrid method to predict the RNA pseudoknot secondary structures by combining detection methods with dynamic programming algorithms. This hybrid method is further enhanced by adopting the case-based reasoning (CBR) technique. Three different methods are proposed, (i) Bioinspired swarm intelligence method (HPRna); (ii) Adaptive hybrid method (MSeeker); and (iii) Fast parallel method (FGTSeeker), where each is an improvement to the previous method. The proposed prediction methods were evaluated against other existing prediction methods using the real native structures as the main factor of comparison. Results show that the three proposed methods obtained more accurate pseudoknotted RNA secondary structures with better performance, especially in predicting long sequences.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/7388
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectEnhancing hybrid computational methodsen_US
dc.subjectfor RNA secondary structure predictionen_US
dc.titleAdapting And Enhancing Hybrid Computational Methods For RNA Secondary Structure Predictionen_US
dc.typeThesisen_US
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Pusat Pengajian Sains Komputer - Tesis