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Applications of Machine Learning to Precision Medicine

Work is presented from two projects, each involving an application of machine learning to precision medicine. The first project was for the Document Triage Task of the BioCreative VI Precision Medicine Track. Teams were asked to build machine learning models to identify journal abstracts that contain at least one mention of a protein-protein interaction (PPI) affected by a mutation. The second project is an analysis of gene expression data from a group of breast cancer patients receiving neoadjuvant chemotherapy to search for biomarkers predicting the outcome of treatment. The model developed for the Biocreative challenge did not use state of the art methods but achieved results only slightly worse than modern deep learning techniques. My contribution to this project was in feature engineering, model tuning and model validation. The feature engineering process will be presented along with a discussion of difficulties due to scarcity of data. The data for the second project was collected from breast cancer patients at the Sun Yat-sen University Cancer Center in Guangzhou China. RNASeq data and clinical information were collected from patients before and after undergoing neoadjuvant chemotherapy. Genes and pathways of potential relevance to the outcome of neoadjuvant therapy were identified for further study. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / 2019 / June 12, 2019. / Biomarkers, Genomics, Machine Learning, Neoadjuvant Chemotherapy, Precision Medicine, Text Mining / Includes bibliographical references. / Jinfeng Zhang, Professor Directing Dissertation; Tingting Zhao, University Representative; Mingjing Tao, Committee Member; Wei Wu, Committee Member.
ContributorsQu, Jinchan (author), Zhang, Jinfeng (professor directing dissertation), Zhao, Tingting (university representative), Tao, Minjing (committee member), Wu, Wei (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Statistics (degree granting departmentdgg)
PublisherFlorida State University
Source SetsFlorida State University
LanguageEnglish, English
Detected LanguageEnglish
TypeText, text, doctoral thesis
Format1 online resource (83 pages), computer, application/pdf

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