Network Medicine

In a cell, the function of most cellular components (genes, proteins, metabolites, micro-RNA, etc.) is brought to bear through the interaction with other cellular components. The set of interactions in a cell can be modeled as complex biological networks, where nodes represent biomolecules and links represent relations between them. The interconnectivity among bio-molecules implies that the relation between the entire set of genes in a cell (genotype) and their physical manifestation (phenotype) is extremely complex. Network medicine is a recent paradigm that exploits the organizing principles of human cellular networks and links network structures to disease.

From a network medicine perspective, hereditary diseases can be seen as perturbations of “disease modules” in the interactome. An important effort in our lab has been aimed at quantifying similarity between hereditable diseases at molecular level by bringing together the existing information that is scattered across the vast corpus of biomedical literature. In other words, we developed a method that quantifies distance between disease modules in the interactome.

Quantifying disease similarity at molecular level enables the transfer of knowledge between similar diseases, providing hypotheses for causal genes discovery and even suggestions for drug repositioning. This is particularly important for hereditary diseases for which no disease gene is currently known – about 30% of them. For these orphan diseases, our measure can help pinpoint the location of their molecular perturbations. Our measure can also be used for differential diagnosis, aiding medical practitioners in identifying putative alternative diagnosis that are obscured by the complexity and multiplicity of the symptoms.

We developed a novel network-based approach to prioritize gene-disease associations that can predict genes for diseases with no known molecular basis by exploiting our phenotypic measure. Our method, which uses semi-supervised learning for the prediction, can accurately predict disease genes for molecularly uncharacterised diseases and also gives excellent results for molecularly characterized diseases, when compared with state-of-the-art methods. Moreover, it can also be used for disease module prediction.

With the onset of the COVID-19 pandemic we began working on methods for drug repositioning against COVID-19 that target SARS-CoV-2 and its cellular processes in the host. We developed a matrix decomposition approach that exploits drug developmental information to predict broad-spectrum antivirals. We also introduced a graph kernel-based approach, rooted in ideas from network medicine, that predicts which FDA-approved drugs are more likely to perturb the human subnetwork that is crucial for SARS-CoV-2 infection/replication. Recently, we have been expanding on these methods, combining ideas from network medicine with collaborative filtering to predict host-centric antivirals for different viruses.

In collaboration with the lab of Giorgio Valentini at the University of Milan, we developed a network-based approach for modelling patients’ biomolecular profiles for clinical phenotype/outcome prediction. Our method builds the profiles in a graph-structured patient space rather than the more typical biomarker space. We construct a network of patients based on their functional or genetic similarities, and then we apply a semi-supervised transductive approach to predict phenotype/clinical outcomes. Extensive tests show that our approach accurately predicts phenotype/outcome in patients with several diseases and provide interpretable results, thus leading to an explainable patient stratification based on their biomolecular characteristics.

In our lab research in Network Medicine has been funded by the BBSRC (grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1)

• Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates
  Nature Communications, 2018.
  
  
• A network medicine approach to quantify distance between hereditary disease modules on the interactome
  Nature Scientific Reports 5, 2015. Article number: 17658.
  
  
• mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome
  Human Mutation; DOI: 10.1002/humu.22963 (2016).
  
  
• An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods
  Artificial intelligence in medicine, vol. 61, iss. 2, pp. 63-78, 2014.
  
  
• LanDis: The Disease Similarity Landscape Explorer
  [In preparation].
  
  
• Combining network medicine and collaborative filtering for predicting host centric antivirals
  [In preparation].