Machine learning for complex biological systems at multiple scales

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Bioengineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Nambiar, Ananthan

Contributors dc:contributor

• Maslov, Sergei
• Anastasio, Mark
• Milenkovik, Olgica
• Lam, Fan

• Machine Learning
• Complex Systems
• Biological Systems
• Systems Biology
• Computational Biology
• Proteins
• Genes
• Liver Disease

Statement dc:rights

• Copyright 2025 Ananthan Nambiar

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130075

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130075