AbstractmicroRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA–mRNA binding. Here, the recently published TarDB database of plant miRNA–mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca. The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). miRNAs have been implicated in numerous biological processes in animals and plants, so discovering miRNAs within unannotated genomes and determining which mRNA they may target are important challenges. MiRNA and miRNA targets can be identified experimentally through costly and time consuming wet-lab verification techniques or computationally using a variety of techniques. In the case of miRNA discovery, several models exist, however, they have been tested and trained data with a class imbalance not indicative of the real-life problem distribution. Several ML miRNA target predictors exist; however, they primarily focus on the Animal Kingdom. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding.In this thesis, we focus on two areas of miRNA research: miRNA discovery and miRNA target prediction. Specifically, we develop machine learning techniques to discover miRNA in the Soy Cyst Nematode genome -a destructive pathogen of soybean and a species for which very few miRNAs are currently known. Considering that, in some species, it has been shown that miRNAs are differentially expressed in response to pathogen stress, we also predict gene targets for each putative miRNA within both SCN and soybean. Additionally, we develop a plant-specific miRNA targeting model and webserver rigorously tested across four plant species. Finally, we explore the applications of domain adaptation on miRNA targeting. I dedicate this thesis to my parents Dr. Samuel and Victoria Ajila, without their love and support none of this would have been possible. A special thanks to my father, who unbeknownst to him has inspired my love for science, engineering, and a general zeal for discovery. I am who I am because of him.I would like to thank my supervisor Dr. James Green. From the beginning his support and encouragement has never wavered. I am privileged to be able to have worked with him and honoured to have been his graduate student.To my brothers Ibukun, Tobi and Ayo, your unwavering belief in me has supported me through my academic career. Thank you for all your help and guidance. To Kerri, your advice has been paramount to my success, thank you so much. I would also like to thank all my friends especially Divine, Marian, Gabby, Montana, and Naomi. You'll truly never know how much your support has meant to me. To Yvonne and Matt, your friendship means the world to me.Most importantly, I thank my Heavenly Father for all that He has done."The LORD is my shepherd; I shall not want. He maketh me to lie down in green pastures: he leadeth me beside the still waters. He restoreth my soul: he leadeth me in the paths of righteousness for his name's sake. Yea, though I walk through the valley of the shadow of death, I will fear no evil: for thou art with me; thy rod and thy staff they...

In this paper, we use an agent-based model (ABM) to run (counter)mobility scenarios to explore which characteristics of intermediate force capabilities (IFC) are relevant to these, and how they can affect outcomes in gray zone conflicts. Using an ABM called Map-Aware Non-Uniform Automata (MANA), developed by the New Zealand Defense Technology Agency, we implemented two scenarios where the friendly forces’ mobility was limited by large groups of civilians. Then, we employed data farming and analytics methods to analyze the data and identify key parameters influencing the outcomes. The main parameters appeared to be the IFC Range, Power (a measure of the duration of the effect), and Crowd Density. Future research could include a wide range of mobility scenarios and possibly a more detailed IFC representation.