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.

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Multidrug resistance protein 2 (MRP2) is the second member the C subfamily in the superfamily of adenosine triphosphate (ATP)-binding cassette (ABC) efflux transporters. MRP2 is a critical player for generation of bile acidindependent bile flow and biliary excretion of glutathione, glucuronate and sulfate conjugates of endo- and xenobiotics. Dysfunctional expression of MRP2 is associated with Dubin-Johnson Syndrome. Pathological and physiological states or xenobiotics change the MRP2 expression level. Under some conditions, expression of the human MRP2 and rat Mrp2 proteins are regulated at the translation level. There are several transcription initiation sites in MRP2/Mrp2 gene. The 5’ untranslated regions (5’UTRs) of MRP2/Mrp2 contains multiple translation start codons. The focus of this study, therefore, was investigation of the translational regulatory mechanisms mediated by the upstream open reading frames (uORF) of MRP2/Mrp2. Using in vitro translation assays and transient cotransfection assays in HepG2 cells, we showed that the rat uORF1 starting at position -109 (relative to the ATG of Mrp2) and the human uORF2 starting at position -105 (relative to the ATG of MRP2) are two major cis-acting inhibitors of translation among the rat and human multiple uORFs, respectively. Translational regulation mediated by the uORFs in the rat Mrp2 mRNA is a combined effect of the leaky scanning model and the reinitiation model, and also results from interaction of the multiple uORFs. In addition, by Ribonuclease Protection Assays (RPA), we detected multiple transcription initiation sites of MRP2/Mrp2 gene in tissues. We also found that the relative abundance of the rat Mrp2 mRNA isoforms with different 5’UTRs differed in the rat liver, kidney, jejunum, ileum, placenta, and lung. This is the first study on the translational regulatory mechanisms of the MRP2/Mrp2 gene.