{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/130075"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/130075","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning for complex biological systems at multiple scales","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","abstract_has_math":false,"creators":["Nambiar, Ananthan"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Maslov, Sergei","Anastasio, Mark","Milenkovik, Olgica","Lam, Fan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-06-09","date_published":"2025-06-09","updated_at":"2026-07-22T22:25:06Z","subjects":["Machine Learning","Complex Systems","Biological Systems","Systems Biology","Computational Biology","Proteins","Genes","Liver Disease"],"languages":["en","eng"],"rights":["Copyright 2025 Ananthan Nambiar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/130075","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Maslov, Sergei","Anastasio, Mark","Milenkovik, Olgica","Lam, Fan"]},{"key":"dc:creator","label":"Author","values":["Nambiar, Ananthan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-06-09","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioengineering"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Complex Systems","Biological Systems","Systems Biology","Computational Biology","Proteins","Genes","Liver Disease"]}]},{"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 2025 Ananthan Nambiar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/130075"]}]},{"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 2027-08-01","The student, Ananthan Nambiar, accepted the attached license on 2025-06-02 at 13:59.","The student, Ananthan Nambiar, submitted this Dissertation for approval on 2025-06-02 at 14:27.","This Dissertation was approved for publication on 2025-06-09 at 09:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22320 on 2025-10-25 at 15:30:42","Understanding biological systems requires modeling the complex, nonlinear interactions among their components. While high-throughput technologies have made vast biological datasets available, interpreting these data remains a challenge. In this thesis, I present machine learning approaches that tackle this problem across multiple biological scales—ranging from protein function, to gene regulation, to genome-wide association studies. At the molecular scale, I develop a Transformer-based protein language model to generate task-agnostic sequence representations. These representations are finetuned for multiple downstream tasks, including protein family classification, protein-protein interaction prediction, and disordered region annotation. The model outperforms or matches state-of-the-art task-specific methods while maintaining generality. At the regulatory scale, I introduce FUN-PROSE, a deep learning framework for predicting condition-specific gene expression in fungi. By integrating promoter sequences with transcription factor expression data, FUN-PROSE captures complex regulatory logic and achieves high accuracy across multiple fungal species. Interpretation of the model reveals biologically relevant sequence motifs and transcription factor–gene interactions, offering insights into gene regulation. At the level of complex trait genetics, I develop a machine learning–guided GWAS framework to identify genetic variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD) in individuals with obesity. By training a machine learning model on clinical and biochemical features to generate a continuous MASLD risk score (the I-MASLD score), I use this quantitative phenotype in a genome-wide association study. This approach recovers known MASLD-associated genes such as PNPLA3 and PTEN, while also implicating novel loci including HERC2 and GRIA3. Together, these models demonstrate how machine learning can leverage patterns in biological data to generate interpretable, predictive models of complex biological systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning for complex biological systems at multiple scales"]}]}],"canonical_facts":{"dc:contributor":["Maslov, Sergei","Anastasio, Mark","Milenkovik, Olgica","Lam, Fan"],"dc:creator":["Nambiar, Ananthan"],"dc:date":["2025-06-09","2025-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","The student, Ananthan Nambiar, accepted the attached license on 2025-06-02 at 13:59.","The student, Ananthan Nambiar, submitted this Dissertation for approval on 2025-06-02 at 14:27.","This Dissertation was approved for publication on 2025-06-09 at 09:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22320 on 2025-10-25 at 15:30:42","Understanding biological systems requires modeling the complex, nonlinear interactions among their components. While high-throughput technologies have made vast biological datasets available, interpreting these data remains a challenge. In this thesis, I present machine learning approaches that tackle this problem across multiple biological scales—ranging from protein function, to gene regulation, to genome-wide association studies. At the molecular scale, I develop a Transformer-based protein language model to generate task-agnostic sequence representations. These representations are finetuned for multiple downstream tasks, including protein family classification, protein-protein interaction prediction, and disordered region annotation. The model outperforms or matches state-of-the-art task-specific methods while maintaining generality. At the regulatory scale, I introduce FUN-PROSE, a deep learning framework for predicting condition-specific gene expression in fungi. By integrating promoter sequences with transcription factor expression data, FUN-PROSE captures complex regulatory logic and achieves high accuracy across multiple fungal species. Interpretation of the model reveals biologically relevant sequence motifs and transcription factor–gene interactions, offering insights into gene regulation. At the level of complex trait genetics, I develop a machine learning–guided GWAS framework to identify genetic variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD) in individuals with obesity. By training a machine learning model on clinical and biochemical features to generate a continuous MASLD risk score (the I-MASLD score), I use this quantitative phenotype in a genome-wide association study. This approach recovers known MASLD-associated genes such as PNPLA3 and PTEN, while also implicating novel loci including HERC2 and GRIA3. Together, these models demonstrate how machine learning can leverage patterns in biological data to generate interpretable, predictive models of complex biological systems."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/130075"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Ananthan Nambiar"],"dc:subject":["Machine Learning","Complex Systems","Biological Systems","Systems Biology","Computational Biology","Proteins","Genes","Liver Disease"],"dc:title":["Machine learning for complex biological systems at multiple scales"],"dc:type":["text"],"thesis:degree_discipline":["Bioengineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}