2 research outputs found
Systems Toxicology: Mining chemical-toxicity signaling paths to enable network medicine
Systems toxicology, a branch of toxicology that studies chemical effects on biological systems, presents exciting knowledge discovery challenges for the information researcher. The exponential increase in availability of genomic and proteomic data in this domain needs to be matched with increasingly sophisticated network analysis approaches. Improved ability to mine complex gene and protein interaction networks may eventually lead to discovery of drugs that target biological sub-networks (‘network medicine’) instead of individual proteins. In this thesis, we have proposed and investigated the use of a maximal edge centrality criterion to discover drug-toxicity signaling paths inside a human protein interaction network. The signaling path detection approach utilizes drug and toxicity information along with two novel edge weighting measures, one based on edge centrality for detected paths and another using differential gene expression between tissues treated with toxicity-inducing drugs and a control set. Drugs known to induce non-immune Neutropenia were analyzed as a test case and common path proteins on discovered signaling paths were evaluated for toxicological significance. In addition to investigating the value of topological connectivity for identification of toxicity biomarkers, the gene expression-based measure led to identification of a proposed biomarker panel for screening new drug candidates. Comparative evaluation of findings from the DTSP approach with standard microarray analysis method showed clear improvements in various performance measures including true positive rate, positive predictive value, negative predictive value and overall accuracy. Comparison of non-immune Neutropenia signaling paths with those discovered for a control set showed increased transcript-level activation of discovered signaling paths for toxicity-inducing drugs. We have demonstrated the scientific value from a systems-based approach for identifying toxicity-related proteins inside complex biological networks. The algorithm should be useful for biomarker identification for any toxicity assuming availability of relevant drug and drug-induced toxicity information.Ph.D., Information Studies -- Drexel University, 201
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Understanding Disease and Disease Relationships Using Transcriptomic Data
As the volume of transcriptomic data continues to increase, so too does its potential to deepen our understanding of disease; for example, by revealing gene expression patterns shared between diseases. However, key questions remain around the strength of the transcriptomic signal of disease and the identification of meaningful commonalities between datasets, which are addressed in this thesis as follows.
The first chapter, Concordance of Microarray Studies of Parkinson’s Disease, examines the agreement between differential expression signatures across 33 studies of Parkinson’s disease. Comparison of these studies, which cover a range of microarray platforms, tissues, and disease models, reveals a characteristic pattern of differential expression in the most highly-affected tissues in human patients. Using correlation and clustering analyses to measure the representativeness of different study designs to human disease, the work described acts as a guideline for the comparison of microarray studies in the following chapters.
In the next chapter, Using Dysregulated Signalling Paths to Understand Disease, gene expression changes are linked on the human signalling network, enabling identification of network regions dysregulated in disease. Applying this method across a large dataset of 141 common and rare diseases identifies dysregulated processes shared between diverse conditions, which relate to known disease- and drug-sharing-relationships.
The final chapter, Understanding and Predicting Disease Relationships Through Similarity Fusion, explores the integration of gene expression with other data types – in this case, ontological, phenotypic, literature co-occurrence, genetic, and drug data – to understand relationships between diseases. A similarity fusion approach is proposed to overcome the differences in data type properties between each space, resulting in the identification of novel disease relationships spanning multiple bioinformatic levels. The similarity of disease relationships between each data type is considered, revealing that relationships in differential expression space are distinct from those in other molecular and clinical spaces.
In summary, the work described in this thesis sets out a framework for the comparative analysis of transcriptomic data in disease, including the integration of biological networks and other bioinformatic data types, in order to further our knowledge of diseases and the relationships between them.PhD funded by the Biotechnology and Biological Sciences Research Council Doctoral Training Partnershi