{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/11285"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/11285","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"Microbiome Analysis in Colorectal Cancer","abstract":"Colorectal cancer (CRC) results from a complex interplay between genes and the environment. Recent studies have focused on the gut microbial population (the microbiota) and its aggregate genome (the microbiome) as one of the environmental players in colorectal tumorigenesis. High-throughput sequencing techniques have added a new dimension to the mining of gut microbiome for biomarkers of CRC and therapeutic targets. Current approaches to microbiome analysis include quantifying the relative abundancies and diversities of microbial populations along with the identification of disease-specific biomarkers.","abstract_html":"Colorectal cancer (CRC) results from a complex interplay between genes and the environment. Recent studies have focused on the gut microbial population (the microbiota) and its aggregate genome (the microbiome) as one of the environmental players in colorectal tumorigenesis. High-throughput sequencing techniques have added a new dimension to the mining of gut microbiome for biomarkers of CRC and therapeutic targets. Current approaches to microbiome analysis include quantifying the relative abundancies and diversities of microbial populations along with the identification of disease-specific biomarkers.","abstract_has_math":false,"creators":["Dadkhah, Ezzat"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-27T19:51:48Z","subjects":["Biology","Bioinformatics","Classification","Colorectal cancer","Machine learning","Microbiome","OTU","Statistical tests"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/11285"],"render_values":[{"text":"hdl:1920/11285","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biology","Bioinformatics","Classification","Colorectal cancer","Machine learning","Microbiome","OTU","Statistical tests"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/11285"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Colorectal cancer (CRC) results from a complex interplay between genes and the environment. Recent studies have focused on the gut microbial population (the microbiota) and its aggregate genome (the microbiome) as one of the environmental players in colorectal tumorigenesis. High-throughput sequencing techniques have added a new dimension to the mining of gut microbiome for biomarkers of CRC and therapeutic targets. Current approaches to microbiome analysis include quantifying the relative abundancies and diversities of microbial populations along with the identification of disease-specific biomarkers."]},{"key":"dc:title","label":"Title","values":["Microbiome Analysis in Colorectal Cancer"]}]}],"canonical_facts":{"dc:date.issued":["2017"],"dc:description.other":["Colorectal cancer (CRC) results from a complex interplay between genes and the environment. Recent studies have focused on the gut microbial population (the microbiota) and its aggregate genome (the microbiome) as one of the environmental players in colorectal tumorigenesis. High-throughput sequencing techniques have added a new dimension to the mining of gut microbiome for biomarkers of CRC and therapeutic targets. Current approaches to microbiome analysis include quantifying the relative abundancies and diversities of microbial populations along with the identification of disease-specific biomarkers."],"dc:identifier":["hdl:1920/11285"],"dc:subject":["Biology","Bioinformatics","Classification","Colorectal cancer","Machine learning","Microbiome","OTU","Statistical tests"],"dc:title":["Microbiome Analysis in Colorectal Cancer"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T19:51:48Z"}