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Item Application of advanced machine learning strategies for biomedical research(2023) Chou, Renee Ti; Cummings, Michael P.; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biomedical research delves deeply into understanding individual health and disease mechanisms. Recent advancements in technologies have further transformed the field with large-scale data sets, enabling data-driven approaches to identify important patterns and relationships from large data sets. However, these data sets are often noisy and unstructured. Moreover, missing values and high dimensionality further complicate the analysis processes aimed at yielding meaningful results. With examples in ocular diseases and malaria, this dissertation presents novel strategies employing machine learning to tackle some of the challenges in biomedical research. In ocular diseases, sustained ocular drug delivery is critical to retain therapeutic levels and improve patient adherence to dosing schedules. To enhance the sustained delivery system, we engineer peptide sequences as an adapter to impart desired properties to ocular drugs. Specifically, we develop machine learning models separately for three properties–melanin binding, cell-penetration, and non-toxicity. We employ data reduction techniques to reduce the number of features while maintaining the machine learning model performance and apply interpretable machine learning techniques to explain model predictions on the three properties. Experimental validation in rabbits show two-fold increase in drug retention time with the selected peptide candidate. The developed machine learning framework can be further tailored to engineer other properties in molecular sequences with a wide variety of potential in biomedical applications. Malaria is an infectious disease caused by protozoan of the genus Plasmodium and has been a burden in global health. Developing malaria vaccines is challenging due to the diversity in parasite antigen sequences, which may lead to immune escape. To facilitate the vaccine development process, we leverage the wealth of systems data collected from various sources. For facile data management, a database is constructed to store the structured data processed from the results of the bioinformatics tools. Due to the small fraction of Plasmodium proteins labeled as known antigens, and the remaining proteins unknown of being antigens or non-antigens, a positive-unlabeled machine learning method is applied to identify potential vaccine antigen candidates. Beyond malaria, our approach provides a promising framework for identifying and prioritizing vaccine antigen candidates for a broad range of disease pathogens.Item Supplementary materials for Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data(2023) Chou, Renee Ti; Ouattara, Amed; Takala-Harrison, Shannon; Cummings, Michael P.Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of unlabeled proteins the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.Item Supplementary materials for positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum(2023) Chou, Renee Ti; Ouattara, Amed; Adams, Matthew; Berry, Andrea A.; Takala-Harrison, Shannon; Cummings, Michael P.Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens identified prior to availability of the P. falciparum genome. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute the most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.Item Supplementary materials for machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery(2023) Chou, Renee Ti; Hsueh, Henry T.; Rai, Usha; Liyanage, Wathsala; Kim, Yoo Chun; Appell, Matthew B.; Pejavar, Jahnavi; Leo, Kirby T.; Davison, Charlotte; Kolodziejski, Patricia; Mozzer, Ann; Kwon, HyeYoung; Sista, Maanasa; Anders, Nicole M.; Hemingway, Avelina; Rompicharla, Sri Vishnu Kiran; Edwards, Malia; Pitha, Ian; Hanes, Justin; Cummings, Michael P.; Ensign, Laura M.; Cummings, Michael P.; Ensign, Laura M.Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We developed a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) was conjugated to brimonidine, an intraocular pressure (IOP)-lowering drug that is prescribed for three times per day topical dosing, IOP reduction was observed for up to 18 days after a single intracameral HR97-brimonidine injection in rabbits. Further, the cumulative IOP-lowering effect was increased ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.