Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

INTEGRATING CHEMICAL, BIOLOGICAL AND PHYLOGENETIC SPACES OF AFRICAN NATURAL PRODUCTS TO UNDERSTAND THEIR THERAPEUTIC ACTIVITY

INTEGRATING CHEMICAL, BIOLOGICAL AND PHYLOGENETIC SPACES OF AFRICAN NATURAL PRODUCTS TO UNDERSTAND THEIR THERAPEUTIC ACTIVITY Fatima Magdi Hamza Baldo This research aims to utilise ligand-based target prediction to (i) understand the mechanism of action of African natural products (ANPs), (ii) help identify patterns of phylogenetic use in African traditional medicine and (iii) elucidate the mechanism of action of phenotypically active small molecules and natural products with anti-trypanosomal activity. In Chapter 2 the objective was to utilise ligand-based target prediction to understand the mechanism of action of natural products (NPs) from African medicinal plants used against cancer. The Random Forest classifier used in this work compares the similarity of the input compounds from the natural product dataset with compound-target combinations in the training set. The more similar they are in structure, the more likely they are to modulate the same target. Natural products from plants used against cancer in Africa were predicted to modulate targets and pathways directly associated with the disease, thus understanding their mechanism of action e.g. “flap endonuclease 1” and “Mcl-1”. The “Keap1-Nrf2 Pathway” and “apoptosis modulation by HSP70”, two pathways previously linked to cancer (which are not currently targeted by marketed drugs, but have been of increasing interest in recent years) were predicted to be modulated by ANPs. In Chapter 3, we aimed to identify phylogenetic patterns in medicinal plant use and the role this plays in predicting medicinal activity. We combined chemical, predicted target and phylogenetic information of the natural products to identify patterns of use for plant families containing plant species used against cancer in African, Malay and Indian (Ayurveda) traditional medicine. Plant families that are close phylogenetically were found to produce similar natural products that act on similar targets regardless of their origin. Additionally, phylogenetic patterns were identified for African traditional plant families with medicinal species used against cancer, malaria and human African trypanosomiasis (HAT). We identified plant families that have more medicinal species than would statistically be expected by chance and rationalised this by linking their activity to their unique phyto-chemistry e.g. the napthyl-isoquinoline alkaloids, uniquely produced by Acistrocladaceae and Dioncophyllaceae, are responsible for anti-malarial and anti-trypanosome activity. In Chapter 4, information from target prediction and experimentally validated targets was combined with orthologue data to predict targets of phenotypically active small molecules and natural products screened against Trypanosoma brucei. The predicted targets were prioritised based on their essentiality for the survival of the T. brucei parasite. We predicted orthologues of targets that are essential for the survival of the trypanosome e.g. glycogen synthase kinase 3 (GSK3) and rhodesain. We also identified the biological processes predicted to be perturbed by the compounds e.g. “glycolysis”, “cell cycle”, “regulation of symbiosis, encompassing mutualism through parasitism” and “modulation of development of symbiont involved in interaction with host”. In conclusion, in silico target prediction can be used to predict protein targets of natural products to understand their molecular mechanism of action. Phylogenetic information and phytochemical information of medicinal plants can be integrated to identify plant families with more medicinal species than would be expected by chance

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity