• Laura Cantini

Single-cell RNA sequencing (scRNAseq) is revolutionizing biology and medicine. The possibility to assess cellular heterogeneity at a previously inaccessible resolution, has profoundly impacted our understanding of development, of the immune system functioning and of many diseases. While scRNAseq is now mature, the single-cell technological development has shifted to other large-scale quantitative measurements, a.k.a. ‘omics’, and even spatial positioning.
Each single-cell omics presents intrinsic limitations and provides a different and complementary information on the same cell. The current main challenge in computational biology is to design appropriate methods to integrate this wealth of information and translate it into actionable biological knowledge.
In this talk, I will discuss three main computational directions currently explored in my team: (i) dimensionality reduction to study cellular heterogeneity simultaneously from multiple omics; (ii) gene network inference to integrate a large range of interactions between the features of various omics and isolate the regulators underlying cellular heterogeneity and (iii) spatially-informed trajectory inference to reconstruct the spatiotemporal landscape underlying cell dynamics.

• Hélène Gleitz, Department of Hematology, Erasmus Medical Center, Rotterdam 3015GD, the Netherlands, Netherlands
• Mayra Luisa Ruiz Tejada Segura, Institute for Computational Genomics, RWTH Aachen University Medical School, Germany
• James Nagai, Institute for Computational Genomics, RWTH Aachen University Medical School, Germany
• Giulia Cesaro, Department of Information Engineering, University of Padova, Padova, Italy., Italy
• Ivan G. Costa, RWTH Aachen University, Germany
• Rebekka Schneider, Department of Cell Biology, Institute for Biomedical Engineering, Faculty of Medicine, RWTH Aachen University, Germany

Understanding disease specific cellular crosstalk is crucial for therapeutic targeting but challenging as signaling pathways dependent on direct physical interactions are lost in current single-cell sequencing protocols. Some technologies, such as Physically Interacting Cells sequencing (PIC-seq) and spatial transcriptomics technologies, provide information on the spatial context of cells, with potential for constructing physical interaction maps. However, computational analysis of cell-cell interaction data remains challenging, as current methods are unable to compare cell-cell interaction between two conditions: homeostasis and disease. Furthermore, although ligand-receptor based cell communication analysis provides an opportunity to functionally characterize these interactions, no approach has yet linked both cell-cell physical interaction and cell communication mechanisms. To address this gap, we introduce Nichesphere, a computational tool to identify disease related physical cell cell interactions and underlying cellular communication networks. We apply Nichesphere to analyze bone marrow (BM) fibrosis PIC-seq data. This analysis revealed molecular niches with increased interactions in BM fibrosis and enabled the characterization of cellular processes, such as extracellular matrix remodeling and immune recruitment, associated with these interactions.

• Saniya Khullar, Waisman Center, University of Wisconsin - Madison, United States
• Xiang Huang, Waisman Center, University of Wisconsin - Madison, United States
• Raghu Ramesh, Waisman Center, University of Wisconsin - Madison, United States
• John Svaren, Waisman Center, University of Wisconsin - Madison, United States
• Daifeng Wang, Waisman Center, University of Wisconsin - Madison, United States

Background: Transcription factor (TF) coordination plays a key role in gene regulation via direct and/or indirect protein–protein interactions (PPIs) and co-binding to regulatory elements on DNA. Single-cell technologies enable gene expression measurement for individual cells and identification of distinct cell types, yet the link between TF-TF coordination and target gene (TG) regulation across diverse cell types remains poorly understood.

Method: To address this, we introduce Network Regression Embeddings (NetREm), an innovative computational approach to uncover cell-type-specific TF-TF coordination activities driving TG regulation. NetREm leverages network-constrained regularization, integrating prior knowledge of TF-TF PPIs with single-cell (or bulk-level) gene expression data. It identifies transcriptional regulatory modules (TRMs) composed of antagonistic/cooperative TF-TF PPIs and predicts novel TF-TG regulatory links complementing state-of-the-art gene regulatory networks (GRNs).

Results: We validate NetREm’s performance through simulation studies and benchmark it across multiple datasets in humans, mice, yeast. NetREm prioritizes biologically meaningful TF-TF coordination networks in 9 peripheral blood mononuclear cell types and 42 immune cell subtypes. Additionally, we apply NetREm to neural cell types (e.g., neurons, glia, Schwann cells) from central and peripheral nervous systems, and to Alzheimer’s disease versus control brains. Top predictions are supported by orthogonal experimental validation data, including: ChIP-seq, CUT&RUN, scATAC-seq, knockout studies, expression QTLs, genome-wide association studies. We further link disease-associated variants to our inferred TRMs and GRNs.

Conclusion: NetREm provides a powerful and interpretable framework to predict cutting-edge GRNs and unprecedented coordination networks in a cell-type-specific manner. Our tool is on GitHub to help propel functional genomics and therapeutic discovery.

• Giulia Cesaro, Department of Information Engineering, University of Padova, Italy
• James Nagai, Institute for Computational Genomics, RWTH Aachen University, Germany
• Giacomo Baruzzo, Department of Information Engineering, University of Padova, Italy
• Barbara Di Camillo, Department of Information Engineering, University of Padova, Italy
• Ivan Costa, Institute for Computational Genomics, RWTH Aachen University, Germany

Recent advances in single-cell RNA sequencing have enabled a detailed exploration of cell-cell communication. Several computational tools infer extracellular signaling via ligand-receptor interactions and associate them with downstream transcription factors and target genes using prior knowledge of signaling pathways. However, most approaches overlook the expression of intermediate signaling genes within individual cells, limiting their ability to reflect cell-specific signal transduction. Furthermore, the high dimensionality and technical noise in single-cell RNA sequencing data, particularly dropout events, make capturing and identifying changes in intracellular pathways difficult.
We introduce CellGOSSIP, a novel framework that integrates single-cell RNA sequencing data with curated biological signaling pathway networks to estimate cell-specific intracellular signaling activity. CellGOSSIP employs a personalized network propagation algorithm over pathway-specific gene graphs, using ligand-receptor interactions as seeds for initiating signal propagation. This approach smooths expression noise and captures pathway dynamics by taking into account gene expression levels and pathway topology.
Our evaluation shows that CellGOSSIP outperforms traditional network propagation-based denoising methods in terms of stability of the reconstructed single-cell matrix to increasing levels of dropout noise in the single-cell RNA sequencing data. In a controlled perturbation experiment of ligand-receptor signaling, CellGOSSIP successfully reconstructs transcription factor activity and identifies distinct pathway activation patterns across experimental conditions. Moreover, embeddings based on CellGOSSIP-inferred signaling profiles uncover functional cell subpopulations that are not discernible using raw gene expression data.

• Sindhura Kommu, Virginia Tech, United States
• Yizhi Wang, Virginia Tech, United States
• Yue Wang, Virginia Tech, United States
• Xuan Wang, Virginia Tech, United States

Motivation: Single-cell RNA sequencing (scRNA-seq) data offers unprecedented opportunities to infer gene regulatory networks (GRNs) at a fine-grained resolution, shedding light on cellular phenotypes at the molecular level. However, the high sparsity, noise, and dropout events inherent in scRNA-seq data pose significant challenges for accurate and reliable GRN inference. The rapid growth in experimentally validated transcription factor-DNA binding data has enabled supervised machine learning methods, which rely on known regulatory interactions to learn patterns, and achieve high accuracy in GRN inference by framing it as a gene regulatory link prediction task. This study addresses the gene regulatory link prediction problem by learning vectorized representations at the gene level to predict missing regulatory interactions. However, a higher performance of supervised learning methods requires a large amount of known TF-DNA binding data, which is often experimentally expensive and therefore limited in amount. Advances in large-scale pre-training and transfer learning provide a transformative opportunity to address this challenge. In this study, we leverage large-scale pre-trained models, trained on extensive scRNA-seq datasets and known as single-cell foundation models (scFMs). These models are combined with joint graph-based learning to establish a robust foundation for gene regulatory link prediction.

Results: We propose scRegNet, a novel and effective framework that leverages scFMs with joint graph-based learning for gene regulatory link prediction. scRegNet achieves state-of-the-art results in comparison with nine baseline methods on seven scRNA-seq benchmark datasets. Additionally, scRegNet is more robust than the baseline methods on noisy training data.

Availability: The source code is available at https://github.com/sindhura-cs/scRegNet

• Ibrahim Alsaggaf, School of Computing and Mathematical Sciences, Birkbeck, University of London, United Kingdom, United Kingdom
• Alex A. Freitas, School of Computing, University of Kent, United Kingdom, United Kingdom
• Cen Wan, School of Computing and Mathematical Sciences, Birkbeck, University of London, United Kingdom, United Kingdom

Protein-protein interaction (PPI) networks are a type of informative feature source that has already been widely used in Bioinformatics research. However, the enormous number of proteins in PPI networks leads to a natural challenge in analytics. Although network embedding methods (e.g. node2vec [1]) recently showed good performance in reducing the extremely high dimensionality of PPI network dataset, the predictive performance of network embeddings still needs to be improved. In this abstract, we introduce a recently proposed contrastive learning algorithm, namely Sup-EGsCL, which exploits an enhanced Gaussian noise augmentation approach to learn a better feature representation space, leading to improved accuracy in predicting the pro-/anti-longevity effects of different model organisms using PPI network-based features. The content of this abstract was published in [2].

• Kivilcim Ozturk, UC San Diego, United States
• Hannah Carter, UC San Diego, United States

Biological functions and cellular behaviors arise from interactions among proteins and other molecules within cells, and cancers often act to perturb these interactions, resulting in disease phenotypes. Many proteins contain intrinsically disordered regions (IDRs) that perform biological functions without relying on a single well-defined conformation. While IDRs of several cancer drivers have emerged as central mediators of oncogenic signaling and post-translational modifications, their role in protein-protein interactions (PPIs) is less clear. Here, we set out to characterize the mutational landscape of IDRs in how they contribute to perturbation of underlying protein interaction networks in cancer. A comprehensive analysis of our structurally resolved PPI network showed that IDRs are enriched for cancer missense mutations. Furthermore, proteins containing IDRs are more centrally located in the network, especially cancer drivers, where disordered drivers are significantly more central than ordered ones, suggesting that the inherent conformational heterogeneity of IDRs might enable them to interact with a wider range of molecular partners, allowing them to easily propagate signals through the cell and the mutations targeting them to generate a larger impact on cellular activity and phenotypes. Finally, using a domain-level cell fitness screen, we discovered that cancer drivers contain IDRs on their interaction interface regions corresponding to significant depletion of cell fitness, pointing to potential cancer cell vulnerabilities. Overall, our work demonstrates the importance of uncovering the systems-level mutational landscape of IDRs to identify mechanisms driving cancer development and progression, enabling more effective selection and development of cancer therapeutics.

• Erik Sonnhammer, Stockholm University, Sweden

We recently released FunCoup 6, a major update to the FunCoup network database, providing researchers with a significantly improved and redesigned platform for exploring the functional coupling interactome.

The FunCoup network database (https://FunCoup.org) contains some of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence combined with orthology transfer. FunCoup’s high coverage comes from using ten different types of evidence, and extensive transfer of information between species.

Key innovations in release 6:

- Enhanced regulatory link coverage: FunCoup 6 now includes over half a million directed gene regulatory links in the human network alone. 13 species in FunCoup now contain regulatory links..

- New website: We completely redesigned the FunCoup website and updated its API functionalities, ​enhancing user accessibility and experience.

- Integrated advanced online tools for network analysis: The integration of TOPAS for disease and drug target module identification, along with network-based KEGG pathway enrichment analysis using ANUBIX, expands the utility of FunCoup 6 for biomedical research.

- New training framework: applied to produce comprehensive networks for 23 primary species and 618 additional orthology-transferred species.

- FunCoup 6 is also available as a Cytoscape app.

A unique feature of both the FunCoup website and the Cytoscape app is the possibility to perform ‘comparative interactomics’ such that subnetworks of different species are aligned using orthologues. FunCoup further demonstrates superior performance compared to other functional association networks, offering researchers enhanced capabilities for studying gene regulation, protein interactions, and disease-related pathways.

• Dewei Hu, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
• Damian Szklarczyk, Department of Molecular Life Sciences, University of Zurich, Switzerland
• Christian Von Mering, Department of Molecular Life Sciences, University of Zurich, Switzerland
• Lars Juhl Jensen, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark

Representation learning has revolutionized sequence-based prediction of protein function and subcellular localization. Protein networks are an important source of information complementary to sequences, but the use of protein networks has proven to be challenging in the context of machine learning, especially in a cross-species setting. To address this, we leveraged the STRING database of protein networks and orthology relations for 1,322 eukaryotes to generate network-based cross-species protein embeddings. We did this by first creating species-specific network embeddings and subsequently aligning them based on orthology relations to facilitate direct cross-species comparisons. We show that these aligned network embeddings ensure consistency across species without sacrificing quality compared to species-specific network embeddings. We also show that the aligned network embeddings are complementary to sequence embedding techniques, despite the use of seqeuence-based orthology relations in the alignment process. Finally, we validated the embeddings by using them for two well-established tasks: subcellular localization prediction and protein function prediction. Training logistic regression classifiers on aligned network embeddings and sequence embeddings improved the accuracy over using sequence alone, reaching performance numbers close to state-of-the-art deep-learning methods. The precomputed cross-species network embeddings and ProtT5 embeddings for all eukaryotic proteins have been included in STRING version 12.0.

• Beatriz Urda-García, Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
• Davide Cirillo, Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
• Alfonso Valencia, Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain

Numerous diseases co-occur more than expected by chance, likely due to a combination of genetic and environmental factors. However, the extent to which these influences shape disease relationships remains unclear. Here, we integrate large-scale RNA-seq data and heritability measures from human diseases with genomic data from the UK Biobank to disentangle the genetic and non-genetic origins of disease co-occurrences (DCs).
Our findings show that gene expression not only recovers but also expands upon genomically explained DCs, capturing disease relationships beyond genetic variation. Approximately 60% of transcriptomically inferred DCs have a detectable genomic component, whereas the remaining 40% are not explained by known genomic layers, suggesting contributions from regulatory or environmental mechanisms. Consistent with this interpretation, the relative contributions of transcriptomics and genomics reconstruct disease etiology and correlate with comorbidity burden, revealing key aspects of disease mechanisms. Complex diseases with strong genetic predispositions tend to be captured by both omics, whereas those primarily influenced by non-genetic factors are better explained by transcriptomics. Additionally, we find that diseases do not generally co-occur based on their heritability, except when sharing SNPs. However, highly heritable diseases tend to have genetically driven co-occurrences, even with lowly heritable diseases. In contrast, transcriptomics explains DCs regardless of heritability, at least partly due to non-heritable mechanisms, such as regulatory or environmental. Integrating transcriptomic and genomic data provides near-complete coverage of DCs among the analyzed diseases, with a considerable portion likely rooted in factors beyond DNA sequence and, therefore, potentially modifiable.

• Lucas Gillenwater, University of Colorado Anschutz Medical Campus, United States
• Lawrence Hunter, University of Chicago, United States
• James Costello, University of Colorado Anschutz Medical Campus, United States

Motivation: Disruption in normal gene expression can contribute to the development of diseases and chronic conditions. However, identifying disease-specific gene signatures can be challenging due to the presence of multiple co-occurring conditions and limited sample sizes. Unsupervised representation learning methods, such as matrix decomposition and deep learning, simplify high-dimensional data into understandable patterns, but often do not provide clear biological explana-tions. Incorporating prior biological knowledge directly can enhance understanding and address small sample sizes. Nevertheless, current models do not jointly consider prior knowledge of mo-lecular interactions and sample labels.
Results: We present GRACKLE, a novel non-negative matrix factorization approach that applies Graph Regularization Across Contextual KnowLedgE. GRACKLE integrates sample similarity and gene similarity matrices based on sample metadata and molecular relationships, respectively. Sim-ulation studies show GRACKLE outperformed other NMF algorithms, especially with increased background noise. GRACKLE effectively stratified breast tumor samples and identified condition-enriched subgroups in individuals with Down syndrome. The model's latent representations aligned with known biological patterns, such as autoimmune conditions and sleep apnea in Down syn-drome. GRACKLE's flexibility allows application to various data modalities, offering a robust solution for identifying context-specific molecular mechanisms in biomedical research.
Availability and implementation: GRACKLE is available at: https://github.com/lagillenwater/GRACKLE

• Viacheslav Dubovitskii, Algorithmiq Ltd, Helsinki, Finland, United States
• Aritra Bose, Quantum for Healthcare and Life Sciences, IBM Research, Yorktown Heights, NY, United States
• Filippo Utro, Quantum for Healthcare and Life Sciences, IBM Research, Yorktown Heights, NY, United States
• Laxmi Parida, Quantum for Healthcare and Life Sciences, IBM Research, Yorktown Heights, NY, United States

Biomolecular networks, such as protein–protein interactions, gene–gene associations, and cell–cell interactions, offer valuable insights into the complex organization of biological systems. These networks are key to understanding cellular functions, disease mechanisms, and identifying therapeutic targets. However, their analysis is challenged by the high dimensionality, heterogeneity, and sparsity of multi-omics data. Random walk algorithms are widely used to propagate information through disease modules, helping to identify disease-associated genes and uncover relevant biological pathways. In this work, we investigate the limitations of classical random walks and explore the potential of quantum random walks (QRW) for biomolecular network analysis. We evaluate QRW in a gene–gene interaction network associated with asthma, autism, and schizophrenia. QRW more accurately rank disease-associated genes compared to classical random walk with restart. Our findings suggest that quantum random walks offer a promising alternative to classical approaches for biomarker discovery, with improved sensitivity to network structure and better performance in identifying biologically relevant features. This highlights their potential in advancing network medicine and systems biology.

• Jan Baumbach

Most heritable diseases are polygenic and yield complex disease mechanisms. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs). Existing statistical methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs / disease genes in a candidate mechanism. We further show that this computationally demanding task can be accelerated with quantum computing. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, we demonstrate the potential of seamlessly integrated quantum computing techniques to accelerate mechanism mining. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of drug repurposing candidates and improved combination therapies.

• Youngoh Kim, Seoul National University, South Korea
• Dongmin Bang, Seoul National University, South Korea
• Bonil Koo, Seoul National University, South Korea
• Jungseob Yi, Seoul National University, South Korea
• Changyun Cho, Seoul National University, South Korea
• Jeonguk Choi, Seoul National University, South Korea
• Sun Kim, Seoul National University, South Korea

Drug–Target Affinity (DTA) prediction is an important regression task for drug discovery, which can provide richer information than traditional drug-target interaction prediction as a binary prediction task. To achieve accurate DTA prediction, quite large amount of data is required for each drug, which is not available as of now. Thus, data scarcity and sparsity is a major challenge. Another important task is `cold-start' DTA prediction for unseen drug or protein. In this work, we introduce MixingDTA, a novel framework to tackle data scarcity by incorporating domain-specific pre-trained language models for molecules and proteins with our MEETA (MolFormer and ESM-based Efficient aggregation Transformer for Affinity) model. We further address the label sparsity and cold-start challenges through a novel data augmentation strategy named GBA-Mixup, which interpolates embeddings of neighboring entities based on the Guilt-By-Association (GBA) principle, to improve prediction accuracy even in sparse regions of DTA space. Our experiments on benchmark datasets demonstrate that the MEETA backbone alone provides up to a 19% improvement of mean squared error over current state-of-the-art baseline, and the addition of GBA-Mixup contributes a further 8.4% improvement. Importantly, GBA-Mixup is model-agnostic, delivering performance gains across all tested backbone models of up to 16.9%. Case studies shows how MixingDTA interpolates between drugs and targets in the embedding space, demonstrating generalizability for unseen drug–target pairs while effectively focusing on functionally critical residues. These results highlight MixingDTA’s potential to accelerate drug discovery by offering accurate, scalable, and biologically informed DTA predictions. The code for MixingDTA is available at https://github.com/rokieplayer20/MixingDTA.

• Nicoleta Siminea, INCDSB; Doctoral School of Computer Science, University of Bucharest, Romania
• Victor Bogdan Popescu, Revvity Finland Oy, Finland
• Ion Petre, Department of Mathematics and Statistics, University of Turku, Finland
• Andrei Păun, INCDSB; Faculty of Mathematics and Computer Science, University of Bucharest, Romania

Glioblastoma is an aggressive cancer with a poor survival rate, and standard treatments often yield limited results. To explore personalized treatment options, we employed network-based analyses. Our aim was to investigate how drug recommendations might vary for individual patients compared to the conventional treatment approaches for glioblastoma.

We began by identifying patient-specific proteins from glioblastoma cases in The Cancer Genome Atlas (TCGA). Using these, we constructed protein–protein interaction networks that incorporated not only the patient-specific proteins but also those encoded by cancer-related genes and known targets of antineoplastic or immunomodulatory drugs. We then applied controllability analysis using NetControl4BioMed to identify proteins that could potentially be targeted with drugs. For each case, we compared the drugs identified through in silico analysis with those predicted to help restore the disease state to a healthy condition.

We also examined the differences between using personalized versus generic networks. Notably, 12% of drugs identified via the generic network appeared in fewer than half of the individual networks. We also found some drugs that, while absent in the generic network, were predicted to offer therapeutic value in individual patients.

This approach enables the construction and analysis of novel individual networks based on proteins identified in new glioblastoma samples. Moreover, the methodology can be adapted for other diseases. For conditions with poor prognoses, enhancing individualized network analyses is essential to improve treatment outcomes.

• Terence Egbelo, University of Sheffield, United Kingdom
• Zeyneb Kurt, University of Sheffield, United Kingdom
• Charlie Jeynes, Evotec UK, United Kingdom
• Mike Bodkin, University of Dundee, United Kingdom
• Val Gillet, University of Sheffield, United Kingdom

Drug discovery faces high clinical failure rates due to adverse events (AEs) from both on- and off-target interactions.
Biomedical knowledge graphs (KGs) integrate domain knowledge in network form. KG completion is the classification task of predicting new relations based on the existing graph.
Our study predicts novel target-AE associations as KG completion using a large-scale biomedical knowledge graph. We incorporated ground truth target-AE associations from multiple sources, including the T-ARDIS database (Galletti et al 2021), into the Drug Repurposing Knowledge Graph (DRKG) by Ioannidis et al (2020).
Rather than ""black-box"" deep learning approaches to KG completion, we employ interpretable ""metapath""-based predictive features that maintain direct reference to domain semantics, following precedents set by Fu et al (2016) and Himmelstein et al (2017).
We introduce a novel approach to address the problem of topological imbalance in KGs, a type of graph data bias. This bias occurs when high-degree entities (nodes in the KG) are overrepresented in the positive ground truth associations, leading to poor prediction performance on less-studied (and thus low-degree) entities—precisely where good inference is most critical. Our bias mitigation method transforms degree sparsity into a useful signal when learning associations of sparsely connected protein targets.
Our approach demonstrated significantly improved AE prediction for the least-studied targets (accuracy increase of ~15% for the bottom 15% of targets by number of AE associations) compared to the well-known Degree-Weighted Path Count (DWPC) method by Himmelstein et al (2017).
Finally, we demonstrate prediction interpretability, including in cases where alternative methods produce errors.

• Johannes Kersting, Technical University of Munich, Germany
• Lisa Spindler, Technical University of Munich, Germany
• Joaquim Aguirre-Plans, STALICLA SL, Spain
• Chloé Bucheron, University of Vienna, Austria
• Quirin Manz, Technical University of Munich, Germany
• Tanja Pock, Technical University of Munich, Germany
• Mo Tan, Technical University of Munich, Germany
• Fernando Delgado-Chaves, University of Hamburg, Germany
• Cristian Nogales, University of Vienna, Austria
• Jan Baumbach, University of Hamburg, Germany
• Jörg Menche, University of Vienna, Austria
• Emre Guney, STALICLA SL, Spain
• Markus List, Technical University of Munich, Germany

Disease modules provide unique insights into the mechanisms of complex diseases and lay the foundation for mechanistic drug repurposing. Algorithms for their identification leverage biological networks to extend an initial set of disease-associated genes (seeds) into subnetworks reflecting biological processes likely to be integral components of the investigated disease. These subnetworks can unveil causal pathways and provide drug repurposing efforts with promising new targets for therapeutics.
Various computational methods have been developed for disease module identification. Since these methods differ in their modeling assumptions and techniques, evaluating various tools across different parameters to optimize for a specific use case is advisable. However, this can be tedious since the individual tools require specific installation and input preparation procedures. Moreover, identifying the best modules is not straightforward and requires topological and biological validation strategies.
To mitigate this, we developed a comprehensive pipeline for disease module identification, evaluation, and subsequent drug prioritization utilizing the workflow system Nextflow. Our pipeline automatically deploys software dependencies using Docker, making installation easy. It prepares the inputs for and runs six popular module detection tools, including DIAMOnD, DOMINO, and ROBUST. The generated outputs are annotated with biological context information, converted into a unified BioPAX format, and extensively evaluated. The latter includes assessing the biological relevance based on overrepresentation analysis and the tool DIGEST, as well as robustness and consistency analyses.
With our contribution, we allow the community to systematically compare different approaches for disease module discovery, thus contributing to robustness and reproducibility in systems and network medicine.

• Samuele Firmani, Helmholtz Center Munich, Germany
• Valter Bergant, Technical University of Munich, Germany
• Corinna Grünke, Institute of Virology, Technical University of Munich, School of Medicine, Munich, Germany
• Yang An, Helmholtz Munich, Germany
• Alexander Henrici, TUM, Germany
• Francesco Paolo Casale, Helmholtz Munich, Germany
• Andreas Pichlmair, Technical University of Munich, Germany
• Annalisa Marsico, Institute of Computational Biology, Helmholtz Zentrum Muenchen, Neuherberg, Germany, Germany

Recent outbreaks of COVID‑19 and monkeypox (Mpox) underscore the need for scalable tools that can disentangle complex host‑virus‑drug interactions and reveal potential therapeutic targets. Although multi‑omics technologies and high‑throughput screens generate rich datasets, integrating these heterogeneous signals to prioritise disease genes remains difficult, especially for poorly understood viruses.
We present GATE, a graph message‑passing neural network (MPNN) that ranks host factors and candidate antiviral drug targets across viral infections. Thanks to the pre‑training phase, GATE learns how extracts maximum value from sparse, weakly labelled data by learning to recognise disease‑related proteins and drug targets within a protein–protein interaction (PPI) network enriched with Gene Ontology functional embeddings, PPI‑derived positional encodings (PEs) and ESM2 language‑model embeddings. The model is then fine‑tuned with virus‑specific multi-omics data.
On both SARS‑CoV‑2 and Mpox tasks, GATE outperformed state‑of‑the‑art models and simpler baselines. Of the architectures evaluated, the Principal Neighbourhood Aggregation (PNA) layer propagated protein features most effectively. Positional encodings were the most informative inputs, while for SARS‑CoV‑2 the protein–viral interactome and effectome data also proved effective. Pre‑training further boosted performance, particularly in the data‑sparse Mpox setting. Predicted host‑factor genes significantly overlapped hits from independent CRISPR‑KO screens and matched validated antiviral targets for both viruses. In addition, GATE’s explanations show underlying biological mechanisms and help prioritise candidates for experimental validation.
GATE is task‑agnostic, scalable and accommodates future omics modalities through modular input feature sets, accelerating discovery and repurposing of antiviral therapeutics.

• Piotr Sliwa, University of Oxford, United Kingdom
• Heather Harrington, University of Oxford, United Kingdom
• Gesine Reinert, University of Oxford, United Kingdom
• Julian C. Knight, University of Oxford, United Kingdom

Integrative analyses of multi-omic patient datasets are crucial to uncover disease subtypes, yet challenges arise from modality‑specific variability and missing data. We propose MLModNet, a multilayer network framework for robust patient stratification. MLModNet employs an extended resampling‑based method (Pareto‑COGENT) to build stable, informative, and modality‑specific patient similarity networks, integrates them into a multiplex network including patients missing individual assays, and detects patient stratification via multiplex‑adapted Leiden clustering.

We applied MLModNet to the COVID‑19 Multi‑omic Blood Atlas (COMBAT) dataset, integrating proteomics, transcriptomics, and cytometry. MLModNet discovered five patient endotypes that refine clinical WHO severity categories, each exhibiting distinct immune‑metabolic signatures involving IL‑33, TREM1, interferon response pathways, and shifts in cell proportions. Clinically, MLModNet clusters significantly stratified ICU‑free survival, and early cluster assignment probabilities predicted subsequent clinical markers (CRP, D‑dimer, Acuity scores). External validation on an independent Olink dataset confirmed the reproducibility of these endotypes and their prognostic relevance. Extensive ablation analyses further supported the robustness of the identified clusters.

MLModNet thus provides a scalable strategy to translate heterogeneous, incomplete multi‑modal data into biologically meaningful, clinically actionable patient stratifications.

• Fiona Brinkman

Microbiome analysis is increasingly becoming a critical component of a wide range of health, agri-foods, and environmental studies. I will present case studies showing the benefit of integrating very diverse metadata into such analyses - and also pitfalls to watch out for. The results of one such cohort study will be further presented, illustrating the need for analyses that allow one to flexibly view the metadata in the context of microbiome data. The results support the multigenerational importance of “healthy", diverse microbiomes, though defining what is “healthy" is complex.