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    Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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    PhD_Dissertation_ArturoMaganaMora.pdf
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    Type
    Dissertation
    Authors
    Magana-Mora, Arturo cc
    Advisors
    Bajic, Vladimir B. cc
    Committee members
    Gojobori, Takashi cc
    Moshkov, Mikhail cc
    Wong, Limsoon
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-04-29
    Embargo End Date
    2018-05-04
    Permanent link to this record
    http://hdl.handle.net/10754/623317
    
    Metadata
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    Access Restrictions
    At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2018-05-04.
    Abstract
    Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often result in extremely intricate ML models. Frequently, these models may have a poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first is to develop a generalizable classification methodology able to systematically derive competitive models despite the complexity and nature of the data. Although several algorithms for the induction of classification models have been proposed, the algorithms are data dependent. Consequently, we developed OmniGA, a novel and generalizable framework that uses different classification models in a treeXlike decision structure, along with a parallel GA for the optimization of the OmniGA structure. Results show that OmniGA consistently outperformed existing commonly used classification models. The second challenge is the prediction of translation initiation sites (TIS) in plants genomic DNA. We performed a statistical analysis of the genomic DNA and proposed a new set of discriminant features for this problem. We developed a wrapper method based on GAs for selecting an optimal feature subset, which, in conjunction with a classification model, produced the most accurate framework for the recognition of TIS in plants. Finally, results demonstrate that despite the evolutionary distance between different plants, our approach successfully identified conserved genomic elements that may serve as the starting point for the development of a generic model for prediction of TIS in eukaryotic organisms. Finally, the third challenge is the accurate prediction of polyadenylation signals in human genomic DNA. To achieve this, we analyzed genomic DNA sequences for the 12 most frequent polyadenylation signal variants and proposed a new set of features that may contribute to the understanding of the polyadenylation process. We derived Omni-PolyA, a model, and tool based on OmniGA for the prediction of the polyadenylation signals. Results show that Omni-PolyA significantly reduced the average classification error rate compared to the state-of-the-art results.
    Citation
    Magana-Mora, A. (2017). Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics. KAUST Research Repository. https://doi.org/10.25781/KAUST-DPDH3
    DOI
    10.25781/KAUST-DPDH3
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-DPDH3
    Scopus Count
    Collections
    Dissertations; Dissertations; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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