• Martina Summer-Kutmon

• Sergio Baranzini

Massive amounts of data and information are available for analysis in biomedicine. However, integration of these resources in a powerful statistical but also biologically meaningful framework poses a considerable challenge. SPOKE is a large knowledge graph containing information from more than 40 specialized databases and spanning multiple disciplines within biomedicine. Currently SPOKE contains 50 million concepts and more than 130 million relationships organized in a semantic graph. This talk will cover the creation of SPOKE and some of its cutting-edge applications. Some examples will include the embedding of more than 2 million electronic health records onto SPOKE, which led to training of machine learning models to predict diagnosis and outcomes in multiple sclerosis (MS), Parkinson’s disease (PD) and Alzheimer’s (AD). In addition, efforts directed towards applications in drug development and repurposing will be presented. Finally, automated integrative strategies are needed to fully harness the power of biomedical information. To that end, a novel method of knowledge graph-based retrieval augmentation (KG-RAG) implemented over SPOKE will be discussed.

• Kevin Rupp, Kevin Rupp, ETH Zurich
• Andreas Lösch, Andreas Lösch, University of Regensburg
• Y. Linda Hu, Y. Linda Hu, University of Regensburg
• Chenxi Nie, Chenxi Nie, ETH Zurich
• Rudolf Schill, Rudolf Schill, ETH Zurich
• Maren Klever, Maren Klever, RWTH Aachen
• Simon Pfahler, Simon Pfahler, University of Regensburg
• Lars Grasedyck, Lars Grasedyck, RWTH Aachen
• Tilo Wettig, Tilo Wettig, University of Regensburg
• Niko Beerenwinkel, Niko Beerenwinkel, ETH Zurich
• Rainer Spang, Rainer Spang, University of Regensburg

Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages
of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer
insights into the underpinnings and the dynamics of metastasis formation. We present metMHN, a cancer progression
model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics
data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical
emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction
of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung
adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study
revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. All datasets and
code are available on GitHub at https://github.com/cbg-ethz/metMHN.

• Deisy Morselli Gysi, Deisy Morselli Gysi, Federal University of Paraná
• Katja Nowick, Katja Nowick, Free University Berlin

The complex nature of mental disorders has long fascinated scientists, driving them to uncover the shared genetic factors that link these conditions. Much is still unknown about the genetic overlap across mental disorders nor the specificities of their genetic underpinning. We create a gene-disease network using genes associated to disorder from multiple curated sources, which revealed clusters of highly genetically related diseases, corroborating with the main chapters of the DSM-5. Interestingly, psychiatric disorders formed a tight cluster with neurodegenerative disorders. This prompts us to investigate that cluster using a combination of gene coexpression networks and protein-protein interaction networks. To this end, we constructed 61 independent coexpression networks, focusing on Transcription Factors, from studies including data from patients with autism spectrum disorder, Bipolar Disorder, Major Depressive Disorder, Schizophrenia, Alzheimer’s Disease and Parkinson's Disease, as well as control individuals, employing rigorous statistical methods to reduce bias between studies and the number of false positive links, and performed a differential network analysis to compare networks across diseases. Our analysis allowed pinpointing signature TF genes for each disorder that could help improve disease diagnosis. Taken together, our discoveries not only advance our understanding of the interconnectedness of the investigated mental disease but also offer the possibility of improving diagnostic approaches to distinguish between diseases, ultimately benefiting individuals affected by these challenging disorders.

• Luis Augusto Eijy Nagai, Luis Augusto Eijy Nagai, University of Tokyo
• Ryuichiro Nakato, Ryuichiro Nakato, University of Tokyo

Recent advances in single-cell RNA sequencing (scRNA-seq) have highlighted the limitations of traditional gene co-expression network analysis in capturing the full spectrum of gene relationships, particularly in terms of negative correlations. Our study introduces an improved community detection method leveraging signed graphs in single-cell gene co-expression networks (scGCNs) to address this gap. We compared the traditional Louvain algorithm with our proposed Louvain Signed method across three distinct tests: a simulated dataset with inherent subgroups, a real dataset of CD4 cell subtypes, and a challenging dataset of ventral midbrain cells exhibiting stemness properties. The Louvain Signed approach demonstrated superior capability in distinguishing nested gene groups, identifying crucial marker genes, and discerning gene communities linked to specific biological functions, even in datasets where cell types were not clearly defined. Our findings suggest that incorporating both positive and negative gene correlations significantly enhances the resolution and relevance of community detection in scGCNs, offering a more nuanced understanding of cellular functions in single-cell studies. This approach promises to refine our understanding of gene dynamics and cellular heterogeneity, complementing existing methods in single-cell analysis.

• Hao Zhu, Hao Zhu, Tufts University
• Donna Slonim, Donna Slonim, Tufts University

Understanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Many existing methods often struggle with the high dimensionality and inherent noise of single-cell data. Inspired by our previous work on dropout augmentation, here, we introduce RegDiffusion, a new class of Denoising Diffusion Probabilistic Models for fast and accurate GRN inference. RegDiffusion introduces Gaussian noise to the input gene expression data following a diffusion schedule and the neural network with a parameterized adjacency matrix is trained to predict the added noise. This approach eliminates costly matrix inversion and significantly accelerates the inference process. Analyzing real world single-cell data with over 14,000 genes now completes in under five minutes, in contrast to the hours required by previous deep learning methods. Further, to verify the biological validity of the inferred networks, we visualized the inferred local regulatory neighborhood around well-studied key genes in mouse microglia cells. We found that genes identified in those neighborhoods are consistent with prior biological knowledge, and genes from the same functional groups are often topologically clustered together. Finally, we would like to demonstrate the regdiffusion package, which includes a straightforward interface to this model and a set of tools to analyze and visualize the inferred GRNs. Overall, with its capacity for rapid inference on large scale data and the explainability of the inferred networks, we believe RegDiffusion will be a useful tool in computational biology and help deliver new insights into complex biological data. Project site: https://tuftsbcb.github.io/RegDiffusion/