Integration of gene expression data with causal networksRajh, Mihael (Avtor) Curk, Tomaž (Mentor) Robyn Bleker, Carissa (Komentor) bioinformaticscausal reasoninggene expressionWith the increased availability of large gene expression datasets comes an increased need for informed methods of data analysis. One class of recent methods involves the use of causal biological networks, which depict causal relationships between molecular events inside the cell. These networks offer the advantage of representing prior biological knowledge in a form that is suited for both computation and human interpretation. However, many of the current methods are held back by implementational challenges, which make them difficult to apply to novel networks. In this thesis, we develop and extend an implementation of the TopoNPA algorithm in the form of a Python package. We present PerturbationX, which features support for custom network syntax, Cytoscape integration, as well as improvements in both edge pruning and permutations. Alongside the implementation, we also provide an estimate of the algorithm's scalability and analyse its sensitivity to noise, missing data, and edge modifications. With the introduction of this robust, open-source tool, we hope to facilitate advancement in the development of causal network algorithms. We aim for the tool to promote insight into experimental data from multiple biological domains.20232023-11-22 08:25:00Magistrsko delo/naloga152352VisID: 35324COBISS_ID: 174454531sl