{"690018":{"#nid":"690018","#data":{"type":"event","title":"PhD Defense by Xinling Li","body":[{"value":"\u003Cp\u003EIn partial fulfillment of the requirements for the degree of\u003C\/p\u003E\u003Cp\u003EDoctor of Philosophy in Bioinformatics\u003C\/p\u003E\u003Cp\u003Ein the Department of for\u0026nbsp;Biomedical Engineering\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EXinling Li\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EWill defend her thesis:\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EQuantitative Approaches for Identifying the Limitation of Single-cell Sequencing Technologies and Applying Sequencing Technologies in the Study of Infectious Diseases and Cancer\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EMay 7, 2026\u003C\/p\u003E\u003Cp\u003E10:00 AM \u2013 12:00 PM (EST)\u003C\/p\u003E\u003Cp\u003EEBB 4029\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/teams.microsoft.com\/meet\/23036548151103?p=UplOBRjEVYTj2NYCAm\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/teams.microsoft.com\/meet\/23036548151103?p=UplOBRjEVYTj2NYCAm\u0022\u003Ehttps:\/\/teams.microsoft.com\/meet\/23036548151103?p=UplOBRjEVYTj2NYCAm\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThesis Advisor:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Qiu Peng\u003C\/p\u003E\u003Cp\u003EDepartment of for\u0026nbsp;Biomedical Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee Members:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Gabe Kwong\u003C\/p\u003E\u003Cp\u003EDepartment of for\u0026nbsp;Biomedical Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Greg Gibson\u003C\/p\u003E\u003Cp\u003ESchool of Biological Sciences\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. May Wang\u003C\/p\u003E\u003Cp\u003EDepartment of for\u0026nbsp;Biomedical Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. M.G. Finn\u003C\/p\u003E\u003Cp\u003ESchool of Chemistry and Biochemistry\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThis thesis focuses on identification of the limitation of single-cell sequencing technologies, and development of pipeline and models for analyzing sequencing data. The aim of this work is to contribute to a deeper understanding of sequencing technologies and a better utilization of the technologies in solving challenges in infectious diseases and cancer research. We first identified systematic under-detection of genes in scRNA-seq and Visium data through analyses of paired bulk RNA-seq\u0026nbsp;and single-cell genomics\u0026nbsp;datasets. These genes were enriched for poly(T) motifs near their 3\u2032 ends, leading to the hypothesis that such motifs may form hairpin structures with poly(A) tails of mRNA transcripts, reducing capture efficiency during library preparation and explaining their lower detection compared to bulk RNA-seq.\u0026nbsp;We also proposed a framework integrating graphical models and conventional machine learning to leverage scRNA-seq data for predicting COVID-19 severity and identifying biomarkers. Graphical models effectively distinguished healthy individuals from infected patients (F1-score \u0026gt; 0.9), while traditional machine learning accurately classified disease severity (F1-score \u0026gt; 0.99), revealing meaningful molecular signatures.\u0026nbsp;In addition, we developed a bioinformatics pipeline to predict\u0026nbsp;existence of\u0026nbsp;proteases across bacterial species by leveraging known bacteria\u2013protease relationships. Through large-scale protein\u2013genome alignments, we identified previously unreported proteases across bacterial genomes that cause severe infectious diseases, with validation from bulk RNA-seq data.\u0026nbsp;Finally, we constructed mathematical models based on mass action kinetics to evaluate multi-arm protease sensors, demonstrating their potential advantages in cancer detection. Overall, this work highlights limitations of current sequencing technologies and provides computational solutions to enhance their utility\u0026nbsp;in early cancer detection and\u0026nbsp;infectious disease\u0026nbsp;treatment and diagnostics.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EQuantitative Approaches for Identifying the Limitation of Single-cell Sequencing Technologies and Applying Sequencing Technologies in the Study of Infectious Diseases and Cancer\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Quantitative Approaches for Identifying the Limitation of Single-cell Sequencing Technologies and Applying Sequencing Technologies in the Study of Infectious Diseases and Cancer"}],"uid":"27707","created_gmt":"2026-04-24 19:13:12","changed_gmt":"2026-04-24 19:14:00","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-05-07T10:00:00-04:00","event_time_end":"2026-05-07T12:00:00-04:00","event_time_end_last":"2026-05-07T12:00:00-04:00","gmt_time_start":"2026-05-07 14:00:00","gmt_time_end":"2026-05-07 16:00:00","gmt_time_end_last":"2026-05-07 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"EBB 4029","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}