Predicting potential drugs and drug-drug interactions for drug repositioning

The purpose of drug repositioning is to predict novel treatments for existing drugs. It saves time and reduces cost in drug discovery, especially in preclinical procedures. In drug repositioning, the challenging objective is to identify reasonable drugs with strong evidence. Recently, benefiting from various types of data and computational strategies, many methods have been proposed to predict potential drugs. Signature-based methods use signatures to describe a specific disease condition and match it with drug-induced transcriptomic profiles. For a disease signature, a list of potential drugs is produced based on matching scores. In many studies, the top drugs on the list are identified as potential drugs and verified in various ways. However, there are a few limitations in existing methods: (1) For many diseases, especially cancers, the tissue samples are often heterogeneous and multiple subtypes are involved. It is challenging to identify a signature from such a group of profiles. (2) Genes are treated as independent elements in many methods, while they may associate with each other in the given condition. (3) The disease signatures cannot identify potential drugs for personalized treatments. In order to address those limitations, I propose three strategies in this dissertation. (1) I employ clustering methods to identify sub-signatures from the heterogeneous dataset, then use a weighting strategy to concatenate them together. (2) I utilize human protein complex (HPC) information to reflect the dependencies among genes and identify an HPC signature to describe a specific type of cancer. (3) I use an HPC strategy to identify signatures for drugs, then predict a list of potential drugs for each patient. Besides predicting potential drugs directly, more indications are essential to enhance my understanding in drug repositioning studies. The interactions between biological and biomedical entities, such as drug-drug interactions (DDIs) and drug-target interactions (DTIs), help study mechanisms behind the repurposed drugs. Machine learning (ML), especially deep learning (DL), are frontier methods in predicting those interactions. Network strategies, such as constructing a network from interactions and studying topological properties, are commonly used to combine with other methods to make predictions. However, the interactions may have different functions, and merging them in a single network may cause some biases. In order to solve it, I construct two networks for two types of DDIs and employ a graph convolutional network (GCN) model to concatenate them together. In this dissertation, the first chapter introduces background information, objectives of studies, and structure of the dissertation. After that, a comprehensive review is provided in Chapter 2. Biological databases, methods and applications in drug repositioning studies, and evaluation metrics are discussed. I summarize three application scenarios in Chapter 2. The first method proposed in Chapter 3 considers the issue of identifying a cancer gene signature and predicting potential drugs. The k-means clustering method is used to identify highly reliable gene signatures. The identified signature is used to match drug profiles and identify potential drugs for the given disease. The second method proposed in Chapter 4 uses human protein complex (HPC) information to identify a protein complex signature, instead of a gene signature. This strategy improves the prediction accuracy in the experiments of cancers. Chapter 5 introduces the signature-based method in personalized cancer medicine. The profiles of a given drug are used to identify a drug signature, under the HPC strategy. Each patient has a profile, which is matched with the drug signature. Each patient has a different list of potential drugs. Chapter 6 propose a graph convolutional network with multi-kernel to predict DDIs. This method constructs two DDI kernels and concatenates them in the GCN model. It achieves higher performance in predicting DDIs than three state-of-the-art methods. In summary, this dissertation has proposed several computational algorithms for drug repositioning. Experimental results have shown that the proposed methods can achieve very good performance

Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases