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| Author(s): |
João Paulo Cassucci dos Santos
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Física de São Carlos (IFSC/BT) |
| Defense date: | 2024-03-08 |
| Examining board members: |
Odemir Martinez Bruno;
Tie Koide;
Thadeu Josino Pereira Penna
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| Advisor: | Odemir Martinez Bruno |
| Abstract | |
Network Science allows us to model multivariate and complex problems in a relatively simple way. This advantage has been shown to be very promising in the context of interdisciplinary researches because it allows us to characterize problems quantitatively that before could only be studied qualitatively. One promising research area for the application of network science is molecular biology, in specific, systems biology, where the context in which the discrete elements belong is more important than their isolated properties. In this dissertation, we intended to explore in two distinct ways the network properties in order to investigate possible biological conclusions that can be extracted from different biomolecular experiments. The first approach uses a new way to measure similarity between vectors known as coincidence index, which was shown to be more effective in the extraction of biological information from enzyme-enzyme interaction networks than the more common correlation measurements traditionally used in these types of modelings, like Pearsons and Spearmans r. The second approach applies new complex network feature extraction techniques, such as Life-Like Network Automata and the Deterministic Tourist Walk, together with machine learning algorithms to classify biological networks datasets that can help in the classification of species and in the prediction of new metabolic pathways. (AU) | |
| FAPESP's process: | 22/06218-7 - Extraction of Biological Information from Topological Measurements of Networks |
| Grantee: | João Paulo Cassucci dos Santos |
| Support Opportunities: | Scholarships in Brazil - Master |