2026-09-18T20:11:55Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32994986 2026-05-01T00:00:00Z portal_693 item_type_8 month_year_05_2026

Knowledge-Guided Machine Learning for Single-Cell Regulatory Genomics Mehrdad Zandigohar (24399968) Bioinformatics Computational biology Genomics Transcription factors (TFs) and cis-regulatory elements coordinate gene regulation, and single-cell sequencing now enables these programs to be studied at high resolution. However, single-cell RNA-seq and ATAC-seq data are sparse and high-dimensional, making it difficult for existing methods to reliably infer TF activity and gene-regulatory networks. This defense presents a set of knowledge-guided machine-learning approaches that embed prior biological evidence into modern analytical models to improve regulatory inference from noisy single-cell data. First, we evaluate TF-IDF transformations and dimensionality-reduction methods for information retrieval in scATAC-seq, showing TF-IDF consistently improves clustering and feature extraction and performs best when paired with variational autoencoders. Second, we extend the BITFAM framework to jointly model scRNA-seq and scATAC-seq in a skin wound-healing study, identifying early and late macrophage subpopulations, cooperative TF communities, and supporting a pro-inflammatory role for NR4A1 with in vivo validation. Third, we introduce scRegulate, a VAE that embeds TF-target priors to infer TF activities and context-specific regulatory networks from scRNA-seq, with benchmarking demonstrating improved recovery of perturbation effects and cell-type-specific programs while scaling approximately linearly with dataset size. Finally, we present RAGulate, a retrieval-augmented generation system that links predicted TF-target relationships to supporting literature and produces an evidence-based confidence score and explanation. Together, these contributions advance interpretable, prior-informed modeling for single-cell regulatory genomics. 2026-05-01T00:00:00Z Text Thesis 10.25417/uic.32994986.v1 https://figshare.com/articles/thesis/Knowledge-Guided_Machine_Learning_for_Single-Cell_Regulatory_Genomics/32994986 In Copyright Open Access after 2028-05-01