Benchmarking network propagation methods for disease gene identification

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version

Doctor of Philosophy

dissertationThe use of the various complementary and alternative medicine (CAM) modalities for the management of chronic illnesses is widespread, and still on the rise. Unfortunately, tools to support consumers in seeking information on the efficacy of these treatments are sparse and incomplete. The goals of this work were to understand CAM information needs in acquiring CAM information, assess currently available information resources, and investigate informatics methods to provide a foundation for the development of CAM information resources. This dissertation consists of four studies. The first was a quantitative study that aimed to assess the feasibility of delivering CAM-drug interaction information through a web-based application. This study resulted in an 85% participation rate and 33% of those patients reported the use of CAMs that had potential interactions with their conventional treatments. The next study aimed to assess online CAM information resources that provide information on drug-herb interactions to consumers. None of the sites scored high on the combination of completeness and accuracy and all sites were beyond the recommended reading level per the US Department of Health and Human Services. The third study investigated information-seeking behaviors for CAM information using an existing cohort of cancer survivors. The study showed that patients in the cohort continued to use CAM well into survivorship. Patients felt very much on their own in dealing with issues outside of direct treatment, which often resulted in a search for options and CAM use. Finally, a study was conducted to investigate two methods to semi-automatically extract CAM treatment relations from the biomedical literature. The methods rely on a database (SemMedDB) of semantic relations extracted from PubMed abstracts. This study demonstrated that SemMedDB can be used to reduce manual efforts, but review of the extracted sentences is still necessary due to a low mean precision of 23.7% and 26.4%. In summary, this dissertation provided greater insight into consumer information needs for CAM. Our findings provide an opportunity to leverage existing resources to improve the information-seeking experience for consumers through high-quality online tools, potentially moving them beyond the reliance on anecdotal evidence in the decision-making process for CAM

Herb-Drug Interactions: A Holistic Decision Support System in Healthcare

Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events