Ph.D. Candidate: Müslüm Kaan Arıcı
Program: Medical Informatics
Date: 22.01.2024 / 14:00
Place: A-108
Abstract: State-of-the-art omics technologies provide molecular insights into various biological contexts, such as disease states, patients, and drug perturbations. Network inference and reconstruction methods use several omics datasets to build contextualized networks with specific biomolecular interactions and activities. In this thesis, we conducted a benchmark analysis to identify deficiencies in reference networks, comparing the coverage of reference networks across various types of knowledge, such as pathways, three-dimensional structure, and publication counts. Additionally, we examined the limitations of reconstruction algorithms by inferring signaling pathways. Contextualized network inference has several challenging issues: i) Hits of omics datasets are sparse in reference networks. ii) Interpretation methods can miss hidden knowledge that connects significant hits in omics datasets while evaluating multi-omics datasets. iii) Well-studied proteins in reference networks come along with bias in contextualization. iv) Highly connected nodes, or hubs, are driven to unspecific and noisy interactions in inferred networks. We developed pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omics data integratioN) to address these challenges, we developed pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omics data integratioN), combining network propagation with graphlets to integrate multi-Omics data. pyPARAGON improves precision and reduces the presence of non-specific interactions in signaling networks by using network motifs in the benchmark tests: reconstruction of cancer signaling pathways and contextualized cancer models. Moreover, pyPARAGON has promising performance in case studies such as tumor-specific networks with significant biological processes and pathways, shared pathways, and different signal strengths in cancer and neurodevelopmental disorders models based on contextualized networks.
