Biology subjects

Eliassi-Rad, T.

Publications and source records attributed to Eliassi-Rad, T..

4 recordsLinked to original sources

bioRxiv2026

Confounding effects of inferring gene co-expression networks from pooled data from different biological populations

Weighted Gene Co-expression Network Analysis (WGCNA) is routinely applied to pooled datasets from multiple biological populations, genotypes, or treatment groups, implicitly assuming a shared module structure across groups. While the distortion of pairwise correlations by pooling heterogeneous groups is well established statistically, three aspects of this problem have received little systematic attention in the context of co-expression network analysis: the extent to which pooling disrupts the discrete module-level community structure inferred by WGCNA; whether this disruption is detectable from the global topology metrics researchers routinely report; and how prevalent the pooling practice is in published multi-group WGCNA studies. Using analytical toy examples and a four-scenario simulation framework, we address all three questions. Module preservation Zsummary scores declined progressively with between-population divergence, from full preservation under identical populations (mean median Zsummary = 25.2 {+/-} 3.3, 95% interval 19.0-30.7 across 20 simulation replicates) to substantial disruption when both network structure and mean expression differed (mean median Zsummary = 11.9 {+/-} 1.0, 95% interval 10.2-13.5). This disruption was undetectable from global topology metrics: modularity and clustering coefficient remained stable across all scenarios, while edge density was sensitive but non-specific. These findings were corroborated in an empirical reanalysis of divergent lake and stream stickleback transcriptomes, where merged analysis collapsed 26 lake-specific and 59 stream-specific modules into only 19 merged modules. A survey of 100 publications found that 78.7% (95% CI 69.4-87.9%) of multi-group WGCNA studies with sufficient methodological reporting used a single merged analysis. Results were robust across network sizes of 250-1,000 genes and rewiring rates of 10-50%. We provide concrete recommendations including module preservation testing in both directions, population-specific baseline networks, and consensus WGCNA as a principled alternative.

bioinformatics↗

bioRxiv2026

Clinical Trial and Ontology-Derived Positive and Negative Benchmark Datasets for Drug Repurposing Across Rare Diseases

Evaluating the potential applications of a medicine is a fundamental challenge in drug development. There is a lack of standardized, decision-oriented benchmarks that test whether computational models can generalize therapeutic hypotheses across diseases in ways that reflect real-world pharmaceutical investment decision making. To address this gap, we introduce two complementary resources: the Indication Expansion Investment Decision Network (IxIDN) and the Orphanet Rare Disease Ontology Negative-network (ORDON). IxIDN is a clinical-trial-derived positive benchmark constructed by projecting drug-disease associations from pharmaceutical clinical trials into a disease-disease network; each edge connects disease pairs that have entered clinical trials for the same drug, thereby capturing cases when concrete indication-expansion decisions have been made. The current release contains 574 rare diseases and 5,336 edges. In contrast, ORDON serves as a stringent, biology-aware negative benchmark derived from the authoritative Orphanet Rare Disease Ontology. It identifies maximally distant disease pairs according to curated hierarchical structure and genetics-linked inheritance patterns, providing 793 rare diseases and 5,000 edges that represent high-separation negative candidates across therapeutic areas. Together, IxIDN and ORDON enable rigorous cross-evidence generalization from clinical trials to disease ontology, testing for Disease- Disease Association Learning (DDAL), a core task for mechanism-centered drug repurposing and indication expansion. All data are publicly available with detailed metadata, enabling reproducible evaluation of models on transparent, decision-relevant benchmarks.

Systems Biology↗

bioRxiv2024

Representation Learning of Human Disease Mechanisms for a Foundation Model in Rare and Common Diseases

A fundamental challenge in translational medicine is the computational modeling of complex human diseases to accelerate therapeutic development. Representation learning provides a powerful framework to address this, yet creating models that capture deep biological mechanisms remains a critical need. To this end, we propose a novel strategy that partitions the disease landscape into rare and non-rare categories, enabling systematic knowledge repurposing both within and between these groups. Here, we introduce Dis2Vec (Disease to Vector), a representation learning framework designed to operationalize this concept. Dis2Vec generates biologically grounded disease embeddings by learning from human genetic and phenotypic data, forming the foundation for Disease-Disease Association Learning (DDAL) and unsupervised disease clustering. We evaluate Dis2Vec representations in two downstream applications. First, we assess DDAL performance on a transfer learning benchmark designed to predict therapeutic transferability, using real-world drug repurposing investment decisions made in clinical trials. Second, unsupervised clustering analyses reveal shared biological mechanisms across diseases. By modeling the disease landscape in this way, Dis2Vec enhances translational research efficiency across both rare and non-rare diseases, advancing the development of foundational models for therapeutic science. Furthermore, Dis2Vec establishes a biologically grounded disease-representation and benchmarking layer that paves the way for trustworthy agentic biomedical AI systems in rare-disease indication expansion.

systems biology↗

bioRxiv2024

Topology-Driven Negative Sampling Enhances Generalizability in Protein-Protein Interaction Prediction

Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcity of quality hard negative samples, shortcut learning, and limited generalizability to novel proteins. Here, we introduce a novel approach for strategic sampling of protein-protein non-interactions (PPNIs) by leveraging higher-order network characteristics that capture the inherent complementarity-driven mechanisms of PPIs. Next, we introduce UPNA-PPI (Unsupervised Pre-training of Node Attributes tuned for PPI), a high throughput sequence-to-function ML pipeline, integrating unsupervised pretraining in protein representation learning with topological PPNI samples, capable of efficiently screening billions of interactions. UPNA-PPI improves PPI prediction generalizability and interpretability, particularly in identifying potential binding sites locations on amino acid sequences, strengthening the prioritization of screening assays and facilitating the transferability of ML predictions across protein families and homodimers. UPNA-PPI establishes the foundation for a fundamental negative sampling methodology in graph machine learning by integrating insights from network topology.

bioinformatics↗