{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121527"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121527","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modeling protein sequences, structures and functions with deep neural networks","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_has_math":false,"creators":["Liu, Yang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Peng, Jian","Han, Jiawei","Zhai, Chengxiang","Liu, Qiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:25:00Z","subjects":["Machine Learning","Deep Learning","Artificial Intelligence","Computational Biology","Bioinformatics"],"languages":["en","eng"],"rights":["Copyright 2023 Yang Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121527","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Jian","Han, Jiawei","Zhai, Chengxiang","Liu, Qiang"]},{"key":"dc:creator","label":"Author","values":["Liu, Yang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-07-14"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Machine Learning","Deep Learning","Artificial Intelligence","Computational Biology","Bioinformatics"]}]},{"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 2023 Yang Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121527"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Yang Liu, accepted the attached license on 2023-07-13 at 19:16.","The student, Yang Liu, submitted this Dissertation for approval on 2023-07-13 at 19:32.","This Dissertation was approved for publication on 2023-07-14 at 10:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19687 on 2023-12-04 at 17:02:36","In the rapid-advancing field of biotechnology, proteins - the fundamental building blocks of life - play a critical role in addressing an array of complex biological challenges. As the cost of experimentation is extremely high and various biological datasets have been created, computational methods for understanding proteins have become essential. In this dissertation, we introduced several machine learning algorithms aiming at improving protein structure and function modeling by leveraging data-driven principles. First, we introduce a deep learning approach for protein contact prediction which uses a deep convolutional network to learn meaningful structural motifs based on experimental data. Second, we detail a data-driven method for learning protein structural representation enabling both high-performance and high-efficiency structural searches. Third, we introduce an end-to-end protein network alignment learning algorithm that integrates heterogeneous information from biological network using graph neural networks. In summary, these developments have demonstrated the potential of applying data-driven principle through novel machine learning algorithms to address the challenges in protein modeling, yielding learned biological insights."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Modeling protein sequences, structures and functions with deep neural networks"]}]}],"canonical_facts":{"dc:contributor":["Peng, Jian","Han, Jiawei","Zhai, Chengxiang","Liu, Qiang"],"dc:creator":["Liu, Yang"],"dc:date":["2023-08","2023-07-14"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Yang Liu, accepted the attached license on 2023-07-13 at 19:16.","The student, Yang Liu, submitted this Dissertation for approval on 2023-07-13 at 19:32.","This Dissertation was approved for publication on 2023-07-14 at 10:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19687 on 2023-12-04 at 17:02:36","In the rapid-advancing field of biotechnology, proteins - the fundamental building blocks of life - play a critical role in addressing an array of complex biological challenges. As the cost of experimentation is extremely high and various biological datasets have been created, computational methods for understanding proteins have become essential. In this dissertation, we introduced several machine learning algorithms aiming at improving protein structure and function modeling by leveraging data-driven principles. First, we introduce a deep learning approach for protein contact prediction which uses a deep convolutional network to learn meaningful structural motifs based on experimental data. Second, we detail a data-driven method for learning protein structural representation enabling both high-performance and high-efficiency structural searches. Third, we introduce an end-to-end protein network alignment learning algorithm that integrates heterogeneous information from biological network using graph neural networks. In summary, these developments have demonstrated the potential of applying data-driven principle through novel machine learning algorithms to address the challenges in protein modeling, yielding learned biological insights."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121527"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Yang Liu"],"dc:subject":["Machine Learning","Deep Learning","Artificial Intelligence","Computational Biology","Bioinformatics"],"dc:title":["Modeling protein sequences, structures and functions with deep neural networks"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}