Biology subjects

Hillerton, T.

Publications and source records attributed to Hillerton, T..

3 recordsLinked to original sources

bioRxiv2025

GeneSNAKE: a Python package for benchmarking and simulation of gene regulatory networks and perturbation-induced expression data

Understanding how genes interact with and regulate each other is a key challenge in systems biology. One of the primary methods to study this is through gene regulatory networks (GRNs). The field of GRN inference however faces many challenges, which necessitates effective tools for evaluating inference methods. For this purpose, data that corresponds to a known GRN, from various conditions and experimental setups is necessary, which is only possible to attain via simulation. Today, most existing tools for GRN-based simulation are limited either in network or data properties, with few or no options to modify these properties. To address these limitations we present GeneSNAKE, a Python package designed to allow users to generate biologically realistic GRNs, and expression data for benchmarking purposes. GeneSNAKE allows the user to control a wide range of network and data properties, including several distinct noise models. GeneSNAKE improves on previous work in the field by adding a perturbation model and a wide range of perturbation schemes along with the ability to control the noise and the perturbation strength. For benchmarking, GeneSNAKE offers a number of functions both for comparing network similarity, and properties in data and GRNs. These functions can further be used to study properties of biological data to produce simulated data with more realistic properties. GeneSNAKE is an open-source, comprehensive simulation and benchmarking package with powerful capabilities that are not combined in any other single package, and thanks to the Python implementation it can be extended and modified by users.

bioinformatics↗

bioRxiv2025

Protein-protein interaction priors shape biologically coherent latent spaces for causally concordant cross-omic translation

Deep learning models routinely compress omics into low-dimensional codes, yet many equally accurate embeddings fail to reflect how cells are wired, which limits explanation and causal reasoning. We present a simple, architecture-agnostic approach to make latent spaces biologically legible: a protein-protein interaction (PPI) prior that softly steers autoencoder units to recruit genes that are proximal on the interactome while discouraging redundant reuse. Applied to large DNA methylation ([~]155k) and RNAseq ([~]993k) compendia and to knowledge-driven (STRING), structure-predicted (RoseTTAFold2-PPI), and union interactomes, this objective reorganizes methylation latents into compact, non-overlapping network neighborhoods without sacrificing reconstruction accuracy. The resulting units map cleanly onto biological processes such as cell-cycle control, immune signaling, proteostasis, mitochondrial metabolism, and RNA handling, with a limited, hub-enriched overlap that plausibly bridges modules. We then asked whether this structured geometry transfers downstream. Using paired TCGA cohorts spanning 23 cancers, omic translators trained on these embeddings, especially a shared-latent bidirectional model, outperformed full-matrix baselines in biologically concordant directions (methylation to transcription, genomics to methylation and transcription) and, crucially, inherited the mechanistic imprint of the upstream encoder. Analytical sensitivity mapping showed that translators fed PPI-guided embeddings preferentially learned known cancer drivers and enriched hallmark pathways, whereas accuracy-matched models trained on non-constrained embeddings did not. Thus, the prior not only regularizes but passes forward a functional coordinate system that makes subsequent predictors mechanistically aware. By keeping biology in the loss rather than hard-wiring it into the network, our approach scales to very large cohorts, preserves flexibility for understudied genes, and yields latents that are both performant and interpretable. More broadly, it outlines a practical route to mechanism-anchored representation learning that propagates explanatory structure into downstream tasks, advancing explainable AI for multi-omic analysis and clinical decision support.

bioinformatics↗

bioRxiv2025

System-level health profiling from blood DNA methylation with explainable deep learning

Genome-scale DNA methylation (DNAm) profiles capture organismal physiology, but most predictive models lack transparency and multi-level applicability. Here we develop an explainable framework that quantifies respiratory, cardiovascular, and metabolic status as bounded health scores (0-1) derived from sex-specific clinical reference ranges and disease penalties, and then predicts these scores from whole-blood DNAm. Using Generation Scotland and case-control samples (n = 14,496 individuals), we screened 39 covariates for disease relevance and DNAm predictability, yielding system- relevant panels that were aggregated into scores. We compressed DNAm profiles with a protein-interaction-guided autoencoder, and trained health predictors on 128- dimensional embeddings using fully connected networks. On held-out samples, models reproduced the composite scores with strong rank agreement (Spearman {rho} = 0.87, R2 = 0.71 for respiratory health; {rho} = 0.82, R2 = 0.66 for cardiovascular; {rho} = 0.81, R2 = 0.64 for metabolic) and recover expected population structure in a generally healthy cohort, with clear separation between "single-system low" and "multi-system low" phenotypes, and graded coupling across systems without redundancy. Further, the top features retrieved from each explainable predictor aligned with system biology: airway epithelial repair, hypoxia and inflammatory trafficking for respiratory; endothelial remodeling and cardiomyocyte programs for cardiovascular; glucose-lipid metabolism and metaflammation for metabolic. These results show that DNAm embeddings can yield accurate, transparent, and system-aware health profiling from blood, providing actionable summaries while revealing the molecular processes the models use to infer multi-system status. This approach positions DNAm embeddings plus interpretable penalty targets as a practical bridge from epigenomic signal to system-level triage and is extensible for evaluation in larger, more diverse cohorts.

bioinformatics↗