Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/94047
Title: Adverse outcome pathway for benzene induced toxicity through reverse causal reasoning and network perturbation analysis
Other Titles: Adverse outcome pathway for benzene induced toxicity through reverse causal reasoning and network perturbation analysis
Authors: Pinho, Xavier Sá Castro
Orientador: Moreira, Irina de Sousa
Stierum, Rob
Keywords: Benzene; Reverse Causal Reasoning; Network Perturbation Analysis; Graphs; Gene Expression; Benzene; Reverse Causal Reasoning; Network Perturbation Analysis; Graphs; Gene Expression
Issue Date: 9-Dec-2020
Serial title, monograph or event: Adverse outcome pathway for benzene induced toxicity through reverse causal reasoning and network perturbation analysis
Place of publication or event: TNO
Abstract: The increase and improvement in molecular profiling technologies have enabled the acquisition of large datasets consisting of measurements for many molecular entities. These datasets allow an understanding of molecular profiles of, for example, a disease, drug and compounds action, or toxicity. Furthermore, gene expression profiling experiments usually produce extensive lists of differential expressed genes that characterize the comparison between the two states in the study, such as disease versus healthy or treatment versus control. In this study two approaches are used to interpret these lists, take out relevant and reliable hypotheses and quantify biological network perturbations: Reverse Causal Reasoning (RCR) and Network Perturbation Analysis (NPA); towards exploring the full potential of these datasets. The RCR and NPA methods are implemented and tested on the transcriptome of benzene-exposed individuals to propose a hypothesis of biological processes alterations. Several proposed altered biological mechanisms are in agreement with literature evidence, meaning that this approach can be a valuable tool for understanding mechanisms associated with benzene exposure. While some of them have not been studied and false positives are a possibility, this approach indicates possible candidates, that have not been verified by the literature as potential future directions in research.
The increase and improvement in molecular profiling technologies have enabled the acquisition of large datasets consisting of measurements for many molecular entities. These datasets allow an understanding of molecular profiles of, for example, a disease, drug and compounds action, or toxicity. Furthermore, gene expression profiling experiments usually produce extensive lists of differential expressed genes that characterize the comparison between the two states in the study, such as disease versus healthy or treatment versus control.In this study two approaches are used to interpret these lists, take out relevant and reliable hypotheses and quantify biological network perturbations: Reverse Causal Reasoning (RCR) and Network Perturbation Analysis (NPA); towards exploring the full potential of this datasets. The RCR and NPA methods are implemented and tested on the transcriptome of benzene-exposed individuals to propose a hypothesis of biological processes alterations.Several proposed altered biological mechanisms are in agreement with literature evidence, meaning that this approach can be a valuable tool for understanding mechanisms associated with benzene exposure. While some of them have not been studied and false positives are a possibility, this approach indicates possible candidates, that have not been verified by the literature as potential future directions in research.
Description: Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia
URI: http://hdl.handle.net/10316/94047
Rights: openAccess
Appears in Collections:UC - Dissertações de Mestrado

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