{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116102"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116102","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Probabilistic subclonal reconstruction for cancer","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_has_math":false,"creators":["Kim, Juho"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi","El-Kebir, Mohammed","Milenkovic, Olgica","Shomorony, Ilan","Chia, Nicholas"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["Cancer genomics","Tumor phylogenetics","Probabilistic modeling","Machine learning"],"languages":["en","eng"],"rights":["Copyright 2022 Juho Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116102","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi","El-Kebir, Mohammed","Milenkovic, Olgica","Shomorony, Ilan","Chia, Nicholas"]},{"key":"dc:creator","label":"Author","values":["Kim, Juho"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-14"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Cancer genomics","Tumor phylogenetics","Probabilistic modeling","Machine learning"]}]},{"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 2022 Juho Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116102"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","The student, Juho Kim, accepted the attached license on 2022-07-14 at 10:17.","The student, Juho Kim, submitted this Dissertation for approval on 2022-07-14 at 10:30.","This Dissertation was approved for publication on 2022-07-14 at 15:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18302 on 2022-11-15 at 21:40:10","Cancer consists of genetically heterogeneous populations of cells that arise through a process of subclonal evolution. Reconstructing the evolutionary processes that give rise to cancer can help us better understand cancer progression and prioritize treatment targets. The subclonal reconstruction of cancer gives us the information about the co-occurrence of mutations within the same subclone, the underlying proportion of cells belonging to each subclone, and the ancestral relationships between them. The evolutionary process can be described by inferring tumor phylogenetic trees. The majority of current approaches focus only on either mutation clustering or tree inference in isolation, or rely on computationally expensive algorithms to holistically consider clustering and tree inference concurrently. In this dissertation, we formalize the problem of reconstructing subclonal structure for cancer via probabilistic modeling. Using variant and total read count obtained from bulk DNA sequencing data as input, we introduce a tree-constrained binomial mixture model and an expectation-maximization (EM) method to estimate the clustering assignment for each mutation and the underlying frequency for each cluster. Our EM algorithm employs a linear programming approach to accurately maximize the likelihood bound subject to tree constraints. We choose the optimal tree topology by repeating the process across all possible tree topologies. Compared to existing work, the resulting ClusTree algorithm more accurately identifies mutation clusters, estimates frequencies for each cluster, and detects the proper tree topology, especially for low-depth sequencing data."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Probabilistic subclonal reconstruction for cancer"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Oluwasanmi","El-Kebir, Mohammed","Milenkovic, Olgica","Shomorony, Ilan","Chia, Nicholas"],"dc:creator":["Kim, Juho"],"dc:date":["2022-08","2022-07-14"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","The student, Juho Kim, accepted the attached license on 2022-07-14 at 10:17.","The student, Juho Kim, submitted this Dissertation for approval on 2022-07-14 at 10:30.","This Dissertation was approved for publication on 2022-07-14 at 15:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18302 on 2022-11-15 at 21:40:10","Cancer consists of genetically heterogeneous populations of cells that arise through a process of subclonal evolution. Reconstructing the evolutionary processes that give rise to cancer can help us better understand cancer progression and prioritize treatment targets. The subclonal reconstruction of cancer gives us the information about the co-occurrence of mutations within the same subclone, the underlying proportion of cells belonging to each subclone, and the ancestral relationships between them. The evolutionary process can be described by inferring tumor phylogenetic trees. The majority of current approaches focus only on either mutation clustering or tree inference in isolation, or rely on computationally expensive algorithms to holistically consider clustering and tree inference concurrently. In this dissertation, we formalize the problem of reconstructing subclonal structure for cancer via probabilistic modeling. Using variant and total read count obtained from bulk DNA sequencing data as input, we introduce a tree-constrained binomial mixture model and an expectation-maximization (EM) method to estimate the clustering assignment for each mutation and the underlying frequency for each cluster. Our EM algorithm employs a linear programming approach to accurately maximize the likelihood bound subject to tree constraints. We choose the optimal tree topology by repeating the process across all possible tree topologies. Compared to existing work, the resulting ClusTree algorithm more accurately identifies mutation clusters, estimates frequencies for each cluster, and detects the proper tree topology, especially for low-depth sequencing data."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116102"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Juho Kim"],"dc:subject":["Cancer genomics","Tumor phylogenetics","Probabilistic modeling","Machine learning"],"dc:title":["Probabilistic subclonal reconstruction for cancer"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}