Biology subjects

Baltusyte, G.

Publications and source records attributed to Baltusyte, G..

3 recordsLinked to original sources

bioRxiv2026

BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration

The growing landscape of Alzheimer's disease (AD) datasets creates opportunities to integrate heterogeneous evidence and systematically discover disease effectors. We present BRIDGE-AD, an interpretable network medicine framework that transforms multimodal data into a unified, disease-specific gene representation for AD effector prioritisation. We integrated more than 30 datasets and curated resources spanning omics, functional, genetic and prior disease knowledge layers. BRIDGE-AD outperformed recently published pretrained and modality-specific gene embeddings in recovering AD-associated genes and produced a genome-wide resource of candidate AD effectors. Established and newly prioritised effectors formed 19 functional clusters, revealing a global molecular landscape of AD biology. BRIDGE-AD supported an SPP1-centred cross-compartment hypothesis and nominated SCARB2, a poorly characterised candidate, for functional validation. SCARB2 rewired lysosomal, lipid-handling and autophagic programmes in microglia, whereas disrupted SCARB2 glycosylation in AD implicated altered SCARB2 processing and function. The accompanying website, https://explore-bridgead.com, enables users to trace the curated evidence and generate mechanistic hypotheses.

bioinformatics↗

bioRxiv2025

A network medicine framework for multi-modal data integration in therapeutic target discovery

The high cost and attrition rate of drug development underscore the need for more effective strategies for therapeutic target discovery. Here, we present a network medicine-based machine learning framework that integrates single-cell transcriptomics, bulk multi-omic profiles, genome-wide CRISPR perturbation screens, and protein-protein interaction networks to systematically prioritise disease-specific targets. Applied to clear cell renal cell carcinoma, the framework successfully recovered established targets and predicted five therapeutic candidates, with subsequent in vitro validation demonstrating that among these, ENO2 inhibition had the strongest anti-tumour effect, followed by LRRK2, a repurposing candidate with phase III Parkinsons disease inhibitors. The proposed approach advances target discovery by moving beyond single-feature, single-modality heuristics to a scalable, machine learning-driven strategy that is generalisable across diseases.

bioinformatics↗

bioRxiv2025

A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma

We present a generalisable, interpretable machine learning framework for therapeutic target discovery using single-cell transcriptomics, protein interaction networks, and drug proximity analysis. The pipeline integrates feature selection via gradient boosting classifiers, systems-level network inference, and in silico drug repurposing, enabling the identification of actionable targets with cellular specificity. As a proof of concept, we apply the method to clear cell renal cell carcinoma (ccRCC), an aggressive kidney cancer with limited treatment options. The model identifies 96 tumour-intrinsic genes, refines them to 16 targets through CRISPR screens and biological curation, and prioritises FDA-approved compounds via network-based proximity scoring. Several novel therapeutic mechanisms - including ABL1, CDK4/6, and JAK inhibition - emerge from this analysis, with predicted compounds showing superior efficacy to standard-of-care drugs across multiple ccRCC cell lines. Beyond ccRCC, this framework offers a scalable strategy for drug discovery across diverse diseases, combining machine learning interpretability with systems biology to accelerate therapeutic development.

systems biology↗