{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1421"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1421","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Parsing genetic models","abstract":"Applications of computer vision have seen great success recently, yet there are few approaches dealing with visual illustrations. We propose a collection of computer vision applications for parsing genetic models. Genetic models are a visual illustration often used in the biological sciences literature. These are used to demonstrate how a discovery fits into what is already known about a biological system. A system that determines the interactions present in a genetic model can be valuable to researchers studying such interactions. The proposed system contains three parts. First, a triplet network is deployed to decide whether or not a figure is a genetic model. Second, a popular object detection network YOLOvS is trained to locate regions of interest within genetic models using various deep learning training techniques. Lastly, we propose an algorithm that can infer the relationships between the pairs of genes or textual features present in the genetic model.","abstract_html":"Applications of computer vision have seen great success recently, yet there are few approaches dealing with visual illustrations. We propose a collection of computer vision applications for parsing genetic models. Genetic models are a visual illustration often used in the biological sciences literature. These are used to demonstrate how a discovery fits into what is already known about a biological system. A system that determines the interactions present in a genetic model can be valuable to researchers studying such interactions. The proposed system contains three parts. First, a triplet network is deployed to decide whether or not a figure is a genetic model. Second, a popular object detection network YOLOvS is trained to locate regions of interest within genetic models using various deep learning training techniques. Lastly, we propose an algorithm that can infer the relationships between the pairs of genes or textual features present in the genetic model.","abstract_has_math":false,"creators":["Lombardo, Michael Natale"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Qureshi, Faisal Z."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-01","date_published":"2022-01-01","updated_at":"2026-07-24T05:35:32Z","subjects":["Diagram understanding","Diagram detection","Visual illustrations","Bioinformatics","Object detection"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1421","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Qureshi, Faisal Z."]},{"key":"dc:creator","label":"Author","values":["Lombardo, Michael Natale"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-03-08T20:15:17Z","2022-03-29T17:27:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-03-08T20:15:17Z","2022-03-29T17:27:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-01-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Diagram understanding","Diagram detection","Visual illustrations","Bioinformatics","Object detection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1421"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Applications of computer vision have seen great success recently, yet there are few approaches dealing with visual illustrations. We propose a collection of computer vision applications for parsing genetic models. Genetic models are a visual illustration often used in the biological sciences literature. These are used to demonstrate how a discovery fits into what is already known about a biological system. A system that determines the interactions present in a genetic model can be valuable to researchers studying such interactions. The proposed system contains three parts. First, a triplet network is deployed to decide whether or not a figure is a genetic model. Second, a popular object detection network YOLOvS is trained to locate regions of interest within genetic models using various deep learning training techniques. Lastly, we propose an algorithm that can infer the relationships between the pairs of genes or textual features present in the genetic model."]},{"key":"dc:title","label":"Title","values":["Parsing genetic models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Qureshi, Faisal Z."],"dc:creator":["Lombardo, Michael Natale"],"dc:date.accessioned":["2022-03-08T20:15:17Z","2022-03-29T17:27:18Z"],"dc:date.available":["2022-03-08T20:15:17Z","2022-03-29T17:27:18Z"],"dc:date.issued":["2022-01-01"],"dc:description.abstract":["Applications of computer vision have seen great success recently, yet there are few approaches dealing with visual illustrations. We propose a collection of computer vision applications for parsing genetic models. Genetic models are a visual illustration often used in the biological sciences literature. These are used to demonstrate how a discovery fits into what is already known about a biological system. A system that determines the interactions present in a genetic model can be valuable to researchers studying such interactions. The proposed system contains three parts. First, a triplet network is deployed to decide whether or not a figure is a genetic model. Second, a popular object detection network YOLOvS is trained to locate regions of interest within genetic models using various deep learning training techniques. Lastly, we propose an algorithm that can infer the relationships between the pairs of genes or textual features present in the genetic model."],"dc:identifier.uri":["https://hdl.handle.net/10155/1421"],"dc:language.iso":["en"],"dc:subject":["Diagram understanding","Diagram detection","Visual illustrations","Bioinformatics","Object detection"],"dc:title":["Parsing genetic models"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:32Z"}