Biology Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2749
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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 Three Variations of Precision Medicine: Gene-Aware Genome Editing, Ancestry-Aware Molecular Diagnosis, and Clone-Aware Treatment Planning(2021) Sinha, Sanju; Ruppin, Eytan; Mount, Steve; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)During my Ph.D., I developed several computational approaches to advance precision medicine for cancer prevention and treatment. My thesis presents three such approaches addressing these emerging challenges by analyzing large-scale cancer omics data from both pre-clinical models and patients datasets. In the first project, we studied the cancer risk associated with CRISPR-based therapies. Therapeutics based on CRISPR technologies (for which the chemistry Nobel prize was awarded in 2020) are poised to become widely applicable for treating a variety of human genetic diseases. However, preceding our work, two experimental studies have reported that genome editing by CRISPR–Cas9 can induce a DNA damage response mediated by p53 in primary cells hampering their growth. This could lead to an undesired selection of cells with pre-existing p53 mutations. Motivated by these findings, we conducted the first comprehensive computational and experimental investigation of the risk of CRISPR-induced selection of cancer gene mutants across many different cell types and lineages. I further studied whether this selection is dependent on the Cas9/sgRNA-delivery method and/or the gene being targeted. Importantly, we asked whether other cancer driver mutations may also be selected during CRISPR-Cas9 gene editing and identified that pre-existing KRAS mutants may also be selected for during CRISPR-Cas9 editing. In summary, we established that the risk of selection for pre-existing p53 or KRAS mutations is non-negligible, thus calling for careful monitoring of patients undergoing CRISPR-Cas9-based editing for clinical therapeutics for pre-existing p53 and KRAS mutations. In the second project, we aimed to delineate some of the molecular mechanisms that may underlie the observed differences in cancer incidences across cancer patients of different ancestries, focusing mainly on lung cancer. We found that lung tumors from African American (AA) patients exhibit higher genomic instability, homologous recombination deficiency, and aggressive molecular features such as chromothripsis. We next demonstrated that these molecular differences extend to many other cancer types. The prevalence of germline homologous recombination deficiency (HRD) is also higher in tumors from AAs, suggesting that at least some of the somatic differences observed may have genetic origins. Importantly, our findings provide a therapeutic strategy to treat tumors from AAs with high HRD, with agents such as PARP and checkpoint inhibitors, which is now further explored by our experimental collaborators. In the third project, we developed a new computational framework to leverage single-cell RNA-seq from patients’ tumors to guide optimal combination treatments that can target multiple clones in the tumor. We first showed that our predicted viability profile of multiple cancer drugs significantly correlates with their targeted pathway activity at a single-cell resolution, as one would expect. We apply this framework to predict the response to monotherapy and combination treatment in cell lines, patient-derived-cell lines, and most importantly, in a clinical trial of multiple myeloma patients. Following these validations, we next charted the landscape of optimal combination treatments of the existing FDA-approved drugs in multiple myeloma, providing a resource that could be used to potentially guide combination trials. Taken together, these results demonstrate the power of multi-omics analysis of cancer data to identify potential cancer risks and a strategy to mitigate, to shed light on molecular mechanisms underlying cancer disparity in AA patients, and point to possible ways to improve their treatment, and finally, we developed a new approach to treat cancer patients based on single-cell transcriptomics of their tumors.