{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1909"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1909","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Knowledge-based artificial neural network modeling assessment: integrating heterogeneous genomics data to uncover lifespan regulation","abstract":"Biological analytics and more advanced data analysis techniques have made remarkable advancements as the area of machine learning continues to grow. More specifically, genetic modeling and neural network building are gaining interest as it becomes a fundamental piece of most model building we see today. We propose a Knowledge-Based Artificial Neural Network (KBANN) to predict phenotype while providing insight to effected subsystems. Within KBANN, the input layers are a single or group of Gene Ontology (GO) terms while each layer’s input is a single number between 0 and 1, explaining how expressed the given term is. The expression number provides an average of the number of copies that a gene is producing at its current age compared to that over the average of its entire lifespan. Preliminary results show that KBANN model can potentially be used to predict lifespan phenotype using the Genotype-Tissue Expression data.","abstract_html":"Biological analytics and more advanced data analysis techniques have made remarkable advancements as the area of machine learning continues to grow. More specifically, genetic modeling and neural network building are gaining interest as it becomes a fundamental piece of most model building we see today. We propose a Knowledge-Based Artificial Neural Network (KBANN) to predict phenotype while providing insight to effected subsystems. Within KBANN, the input layers are a single or group of Gene Ontology (GO) terms while each layer’s input is a single number between 0 and 1, explaining how expressed the given term is. The expression number provides an average of the number of copies that a gene is producing at its current age compared to that over the average of its entire lifespan. Preliminary results show that KBANN model can potentially be used to predict lifespan phenotype using the Genotype-Tissue Expression data.","abstract_has_math":false,"creators":["Day, Taylor"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Qin, Hong","Liang, Yu; Wu, Dalei; Yang, Li","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:47:06Z","subjects":["Genetics--Mathematical models","Neural networks (Computer science)"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/741","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Qin, Hong","Liang, Yu; Wu, Dalei; Yang, Li","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Day, Taylor"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Genetics--Mathematical models","Neural networks (Computer science)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/741"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computer Science and Engineering","M. A.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Arts."]},{"key":"dc:description.abstract","label":"Abstract","values":["Biological analytics and more advanced data analysis techniques have made remarkable advancements as the area of machine learning continues to grow. More specifically, genetic modeling and neural network building are gaining interest as it becomes a fundamental piece of most model building we see today. We propose a Knowledge-Based Artificial Neural Network (KBANN) to predict phenotype while providing insight to effected subsystems. Within KBANN, the input layers are a single or group of Gene Ontology (GO) terms while each layer’s input is a single number between 0 and 1, explaining how expressed the given term is. The expression number provides an average of the number of copies that a gene is producing at its current age compared to that over the average of its entire lifespan. Preliminary results show that KBANN model can potentially be used to predict lifespan phenotype using the Genotype-Tissue Expression data."]},{"key":"dc:title","label":"Title","values":["Knowledge-based artificial neural network modeling assessment: integrating heterogeneous genomics data to uncover lifespan regulation"]}]}],"canonical_facts":{"dc:contributor":["Qin, Hong","Liang, Yu; Wu, Dalei; Yang, Li","College of Engineering and Computer Science"],"dc:creator":["Day, Taylor"],"dc:date":["2022-05-01T07:00:00Z"],"dc:description":["Dept. of Computer Science and Engineering","M. A.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Arts."],"dc:description.abstract":["Biological analytics and more advanced data analysis techniques have made remarkable advancements as the area of machine learning continues to grow. More specifically, genetic modeling and neural network building are gaining interest as it becomes a fundamental piece of most model building we see today. We propose a Knowledge-Based Artificial Neural Network (KBANN) to predict phenotype while providing insight to effected subsystems. Within KBANN, the input layers are a single or group of Gene Ontology (GO) terms while each layer’s input is a single number between 0 and 1, explaining how expressed the given term is. The expression number provides an average of the number of copies that a gene is producing at its current age compared to that over the average of its entire lifespan. Preliminary results show that KBANN model can potentially be used to predict lifespan phenotype using the Genotype-Tissue Expression data."],"dc:identifier":["https://scholar.utc.edu/theses/741"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Genetics--Mathematical models","Neural networks (Computer science)"],"dc:title":["Knowledge-based artificial neural network modeling assessment: integrating heterogeneous genomics data to uncover lifespan regulation"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:06Z"}