{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127281"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127281","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Imputing metabolomics with graph denoising autoencoders","abstract":"The student, Kowshika Sarker, accepted the attached license on 2024-12-06 at 19:10.","abstract_html":"The student, Kowshika Sarker, accepted the attached license on 2024-12-06 at 19:10.","abstract_has_math":false,"creators":["Sarker, Kowshika"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-09","date_published":"2024-12-09","updated_at":"2026-07-22T22:25:03Z","subjects":["Metabolomics","Imputation","Graph Denoising Autoencoder","Graph Neural Network"],"languages":["en","eng"],"rights":["Copyright 2024 Kowshika Sarker"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127281","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Sarker, Kowshika"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-09","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Metabolomics","Imputation","Graph Denoising Autoencoder","Graph Neural Network"]}]},{"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 2024 Kowshika Sarker"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127281"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Kowshika Sarker, accepted the attached license on 2024-12-06 at 19:10.","The student, Kowshika Sarker, submitted this Thesis for approval on 2024-12-06 at 19:20.","This Thesis was approved for publication on 2024-12-09 at 17:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21540 on 2025-03-28 at 14:28:33","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","Metabolomics is an efficacious modality to extract impactful insights in numerous biomedical applications. Metabolomic datasets often exhibit significant sparsity containing many missing entries. Over the years, numerous metabolomic imputation approaches have been studied. Denoising autoencoders have proven powerful for analyzing noisy data in various domains. On the other hand, graph representations are very popular in biochemical research. In this work, we study the efficacy of graph denoising autoencoders (GDAEs) - the mechanism of which is an integration of denoising autoencoders with graph representations - for the imputation of metabolomic data, as this potential avenue is yet unexplored. We propose a GDAE-based metabolomics imputation approach and benchmark it on three metabolomic datasets comparing with the imputation quality of eight existing methods. We also benchmark the imputation methods based on their effect on downstream classification and clustering tasks. We simulate different patterns and proportions of missing entries in metabolomes and compare the level of difficulty of imputation for different missingness. We also inspect the effect of imputation quality on downstream performance. Based on the empirical evidence, we conclude that GDAE-based imputation is an impactful data preprocessing paradigm for metabolomic analyses."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Imputing metabolomics with graph denoising autoencoders"]}]}],"canonical_facts":{"dc:contributor":["Zhai, ChengXiang"],"dc:creator":["Sarker, Kowshika"],"dc:date":["2024-12-09","2024-12"],"dc:description":["The student, Kowshika Sarker, accepted the attached license on 2024-12-06 at 19:10.","The student, Kowshika Sarker, submitted this Thesis for approval on 2024-12-06 at 19:20.","This Thesis was approved for publication on 2024-12-09 at 17:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21540 on 2025-03-28 at 14:28:33","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","Metabolomics is an efficacious modality to extract impactful insights in numerous biomedical applications. Metabolomic datasets often exhibit significant sparsity containing many missing entries. Over the years, numerous metabolomic imputation approaches have been studied. Denoising autoencoders have proven powerful for analyzing noisy data in various domains. On the other hand, graph representations are very popular in biochemical research. In this work, we study the efficacy of graph denoising autoencoders (GDAEs) - the mechanism of which is an integration of denoising autoencoders with graph representations - for the imputation of metabolomic data, as this potential avenue is yet unexplored. We propose a GDAE-based metabolomics imputation approach and benchmark it on three metabolomic datasets comparing with the imputation quality of eight existing methods. We also benchmark the imputation methods based on their effect on downstream classification and clustering tasks. We simulate different patterns and proportions of missing entries in metabolomes and compare the level of difficulty of imputation for different missingness. We also inspect the effect of imputation quality on downstream performance. Based on the empirical evidence, we conclude that GDAE-based imputation is an impactful data preprocessing paradigm for metabolomic analyses."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127281"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Kowshika Sarker"],"dc:subject":["Metabolomics","Imputation","Graph Denoising Autoencoder","Graph Neural Network"],"dc:title":["Imputing metabolomics with graph denoising autoencoders"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}