{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120548"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120548","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Application of machine learning for the creation of a gut microbial biomarker panel enabling personalized nutrition recommendations","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Shinn, Leila Marie"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Nutritional Sciences","degree_department":null,"school":null,"contributors":["Holscher, Hannah D","Donovan, Sharon M","Madak-Erdogan, Zeynep","Rodriguez-Zas, Sandra L","Zhu, Ruoqing"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Gastrointestinal Microbiota","Metabolomics","Metagenomics","Multiomics","Fidelity Measures","Dietary Intake Biomarker","Machine Learning","Multiclass"],"languages":["en","eng"],"rights":["Copyright 2023 Leila Marie Shinn"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120548","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Holscher, Hannah D","Donovan, Sharon M","Madak-Erdogan, Zeynep","Rodriguez-Zas, Sandra L","Zhu, Ruoqing"]},{"key":"dc:creator","label":"Author","values":["Shinn, Leila Marie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-24"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nutritional Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gastrointestinal Microbiota","Metabolomics","Metagenomics","Multiomics","Fidelity Measures","Dietary Intake Biomarker","Machine Learning","Multiclass"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Leila Marie Shinn"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120548"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Leila Shinn, accepted the attached license on 2023-04-23 at 15:21.","The student, Leila Shinn, submitted this Dissertation for approval on 2023-04-23 at 15:27.","This Dissertation was approved for publication on 2023-04-24 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19122 on 2023-09-01 at 17:21:29","Diet affects the human fecal microbiota, metagenome, and metabolome. This effort aims to identify a compact set of highly predictive objective microbial biomarkers of food intake utilizing pre- and post-intervention fecal samples from five completed clinical trials examining almond, avocado, broccoli, walnut, and whole-grain barley and oat intake. First, we used random forest models to predict consumption of these six foods with 70% accuracy using 22 taxa from 16S rRNA sequencing data. Collapsing barley and oats into 1 grains category increased the model accuracy to 77% (23 taxa). Overall model accuracy was 85% (15 taxa) for classifying almond (76% accurate), avocado (88% accurate), walnut (72% accurate), and whole grains (96% accurate) intake (1)(Chapter 3). Second, metagenomic analyses revealed differentially abundant Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) categories in the almond (n = 54), broccoli (n = 2,474), and walnut (n = 732) groups. Using differentially abundant KOs in a random forest model led to prediction accuracies of 80% (almond), 87% (broccoli), and 86% (walnut) compared to their respective controls. A mixed-food random forest model revealed 81% prediction accuracy in differentiating these three foods from one another (2)(Chapter 4). Third, using 96 fecal metabolites, these six foods were differentiated from their respective control groups with prediction accuracies between 47%-89% and almond intake from walnut intake with 91% accuracy (3)(Chapter 5). Finally, our multi-omic analyses revealed 1,356 microbial species, 14,109 KOs, and 96 metabolites that achieved accuracies of 79% (almond), 87% (broccoli), and 94% (walnut) in a random forest model (Chapter 6). This effort provides groundwork for establishing non-invasive, fecal biomarkers of food intake, which may eventually be used as fidelity measures for dietary compliance and the development of personalized diet-microbiota tailored therapies for disease treatment and prevention."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Application of machine learning for the creation of a gut microbial biomarker panel enabling personalized nutrition recommendations"]}]}],"canonical_facts":{"dc:contributor":["Holscher, Hannah D","Donovan, Sharon M","Madak-Erdogan, Zeynep","Rodriguez-Zas, Sandra L","Zhu, Ruoqing"],"dc:creator":["Shinn, Leila Marie"],"dc:date":["2023-05","2023-04-24"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Leila Shinn, accepted the attached license on 2023-04-23 at 15:21.","The student, Leila Shinn, submitted this Dissertation for approval on 2023-04-23 at 15:27.","This Dissertation was approved for publication on 2023-04-24 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19122 on 2023-09-01 at 17:21:29","Diet affects the human fecal microbiota, metagenome, and metabolome. This effort aims to identify a compact set of highly predictive objective microbial biomarkers of food intake utilizing pre- and post-intervention fecal samples from five completed clinical trials examining almond, avocado, broccoli, walnut, and whole-grain barley and oat intake. First, we used random forest models to predict consumption of these six foods with 70% accuracy using 22 taxa from 16S rRNA sequencing data. Collapsing barley and oats into 1 grains category increased the model accuracy to 77% (23 taxa). Overall model accuracy was 85% (15 taxa) for classifying almond (76% accurate), avocado (88% accurate), walnut (72% accurate), and whole grains (96% accurate) intake (1)(Chapter 3). Second, metagenomic analyses revealed differentially abundant Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) categories in the almond (n = 54), broccoli (n = 2,474), and walnut (n = 732) groups. Using differentially abundant KOs in a random forest model led to prediction accuracies of 80% (almond), 87% (broccoli), and 86% (walnut) compared to their respective controls. A mixed-food random forest model revealed 81% prediction accuracy in differentiating these three foods from one another (2)(Chapter 4). Third, using 96 fecal metabolites, these six foods were differentiated from their respective control groups with prediction accuracies between 47%-89% and almond intake from walnut intake with 91% accuracy (3)(Chapter 5). Finally, our multi-omic analyses revealed 1,356 microbial species, 14,109 KOs, and 96 metabolites that achieved accuracies of 79% (almond), 87% (broccoli), and 94% (walnut) in a random forest model (Chapter 6). This effort provides groundwork for establishing non-invasive, fecal biomarkers of food intake, which may eventually be used as fidelity measures for dietary compliance and the development of personalized diet-microbiota tailored therapies for disease treatment and prevention."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120548"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Leila Marie Shinn"],"dc:subject":["Gastrointestinal Microbiota","Metabolomics","Metagenomics","Multiomics","Fidelity Measures","Dietary Intake Biomarker","Machine Learning","Multiclass"],"dc:title":["Application of machine learning for the creation of a gut microbial biomarker panel enabling personalized nutrition recommendations"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Nutritional Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}