The elucidation of the genetic causes of diseases is central to understanding the mechanisms of action of a pathology and the development of treatments. Disease gene prediction methods streamline the discovery of the molecular basis for a disease by prioritizing genes for experimental validation. Technological advances, such as high throughput sequencing and screening techniques, have led to an increasing accumulation of genomic data. Despite this growth, the mechanisms of action through which genomic variants drive disease development are not fully understood. Earlier approaches to find non-experimental disease gene associations such as linkage analysis or genome-wide association studies, produce either limited results or hundreds of candidates, making experimental validation expensive and time consuming. Modern biological networks have been exploited to capture significant features of the highly complex protein interactions, leading to the rise of computational methods in network medicine. Recent network medicine based approaches bypass the lack of functional annotation by drawing inferences from interaction data. My approach, called Cardigan (ChARting DIsease Gene AssociatioNs), is based on a semi-supervised algorithm that propagates labels on the interactome. These labels integrate disease phenotypic information expressed as a similarity measure between diseases, which is obtained by mining and comparing MeSH terms relevant for each disease on the MeSH ontology. Thorough experimentation shows that Cardigan vastly outperforms state-of-the-art disease gene prioritisation methods. This work additionally presents an exploratory extension of the approach, which, to the best of my knowledge, allows network methods for the first time to handle protein interfaces. As a ramification of disease characterisation, this work presents an analysis of viral induced lymphoid malignancies on mice. In particular, the characterisation of the clonality of viral insertions to classify different stages of lymphomagenesis. The results show that we can identify rare driver mutations from late stage samples, with infrequent occurrences as clonal mutations, by adding statistical support of their occurrence as subclonal mutations. Several known rare cancer drivers were found to appear as subclonal mutations in late stage cancer samples more often than expected by random chance. Another research ramification focuses on a pipeline to infer

Network medicine characterisation of genetic disorders by propagation of disease phenotypic similarities

Chagas disease is a parasitic disease, endemic in South America. As of today, there is no effective treatment in its chronic stage. We have recently identified 134 FDA approved drugs with potential antitrypanosomal activity. In this paper, we propose a novel method for selecting combinations of drugs (drug cocktails), to provide a more effective treatment against Chagas disease. We define three measures to evaluate the predicted performance of a cocktail, establishing in this way a mathematical foundation for its analysis. This allows us to model the drug cocktail selection as a multi-objective optimisation problem, that we show can be solved efficiently with state-of-the-art evolutionary algorithms. Our analysis retrieves 57 drug cocktails containing between 2 and 6 drugs. We discuss the improvement of the cocktail selection given by our method, and the application of this approach to the identification of cocktails against other parasitic diseases.

Drug cocktail selection for the treatment of chagas disease: A multi-objective approach

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Drug cocktail selection for the treatment of chagas disease: A multi-objective approach