{"id":{"repo_id":"sask","oai_identifier":"oai:harvest.usask.ca:10388/13096"},"canonical_url":"https://search.dev.ndltd.org/etd/sask/oai:harvest.usask.ca:10388/13096","repository":{"repo_id":"sask","name":"University of Saskatchewan","base_url":"https://harvest.usask.ca/server/oai/request"},"display":{"title":"Improved Inference of Ecological Interaction Types","abstract":"Inference of microbial interaction types allows us to understand the growth and development of microbial life forms found on earth. Numerous methods have been proposed to infer the interaction type(s) of microbes in a microbial communities using a population dynamics model. However, due to dynamic behaviour of microbial communities, these methods can result in erroneous inferences. A method proposed by Xiao et al. in 2017 models the dynamic behaviour of microbial community using sample abundance data overcomes many of these issues, but suffers from a high failure rate of inference, lower confidence on inferred interactions and slower execution speed than the existing algorithms. In this thesis, we propose an improved and more efficient and effective approach to infer the microbial interaction types of larger microbial communities (N>10). Our findings demonstrate that our approach is faster, more fault tolerant, more scalable than the state of the art from 2017, and it has the ability to infer microbial interactions with increased confidence.","abstract_html":"Inference of microbial interaction types allows us to understand the growth and development of microbial life forms found on earth. Numerous methods have been proposed to infer the interaction type(s) of microbes in a microbial communities using a population dynamics model. However, due to dynamic behaviour of microbial communities, these methods can result in erroneous inferences. A method proposed by Xiao et al. in 2017 models the dynamic behaviour of microbial community using sample abundance data overcomes many of these issues, but suffers from a high failure rate of inference, lower confidence on inferred interactions and slower execution speed than the existing algorithms. In this thesis, we propose an improved and more efficient and effective approach to infer the microbial interaction types of larger microbial communities (N>10). Our findings demonstrate that our approach is faster, more fault tolerant, more scalable than the state of the art from 2017, and it has the ability to infer microbial interactions with increased confidence.","abstract_has_math":false,"creators":["Aziz, Syed Umair"],"institution":"University of Saskatchewan","degree_name":"Master of Science (M.Sc.)","degree_level":"Masters","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Stanley, Kevin"],"committee_chairs":[],"committee_members":["Kusalik, Tony","Siciliano, Steven","Mondal, Debajyoti","Peak, Derek"],"year":2020,"date_issued":"2020-10-09","date_published":"2020-10-09","updated_at":"2026-07-24T04:27:16Z","subjects":["microbial interactions","unsupervised"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10388/13096","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Stanley, Kevin"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kusalik, Tony","Siciliano, Steven","Mondal, Debajyoti","Peak, Derek"]},{"key":"dc:creator","label":"Author","values":["Aziz, Syed Umair"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-10-09T20:12:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-10-09T20:12:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-10-09"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.Sc.)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Saskatchewan"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["microbial interactions","unsupervised"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10388/13096"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Inference of microbial interaction types allows us to understand the growth and development of microbial life forms found on earth. Numerous methods have been proposed to infer the interaction type(s) of microbes in a microbial communities using a population dynamics model. However, due to dynamic behaviour of microbial communities, these methods can result in erroneous inferences. A method proposed by Xiao et al. in 2017 models the dynamic behaviour of microbial community using sample abundance data overcomes many of these issues, but suffers from a high failure rate of inference, lower confidence on inferred interactions and slower execution speed than the existing algorithms. In this thesis, we propose an improved and more efficient and effective approach to infer the microbial interaction types of larger microbial communities (N>10). Our findings demonstrate that our approach is faster, more fault tolerant, more scalable than the state of the art from 2017, and it has the ability to infer microbial interactions with increased confidence."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Improved Inference of Ecological Interaction Types"]}]}],"canonical_facts":{"dc:contributor.advisor":["Stanley, Kevin"],"dc:contributor.committeemember":["Kusalik, Tony","Siciliano, Steven","Mondal, Debajyoti","Peak, Derek"],"dc:creator":["Aziz, Syed Umair"],"dc:date.accessioned":["2020-10-09T20:12:07Z"],"dc:date.available":["2020-10-09T20:12:07Z"],"dc:date.issued":["2020-10-09"],"dc:description.abstract":["Inference of microbial interaction types allows us to understand the growth and development of microbial life forms found on earth. Numerous methods have been proposed to infer the interaction type(s) of microbes in a microbial communities using a population dynamics model. However, due to dynamic behaviour of microbial communities, these methods can result in erroneous inferences. A method proposed by Xiao et al. in 2017 models the dynamic behaviour of microbial community using sample abundance data overcomes many of these issues, but suffers from a high failure rate of inference, lower confidence on inferred interactions and slower execution speed than the existing algorithms. In this thesis, we propose an improved and more efficient and effective approach to infer the microbial interaction types of larger microbial communities (N>10). Our findings demonstrate that our approach is faster, more fault tolerant, more scalable than the state of the art from 2017, and it has the ability to infer microbial interactions with increased confidence."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10388/13096"],"dc:subject":["microbial interactions","unsupervised"],"dc:title":["Improved Inference of Ecological Interaction Types"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science (M.Sc.)"],"thesis:institution_name":["University of Saskatchewan"]},"updated_at":"2026-07-24T04:27:16Z"}