Dissertation/ Thesis
Data integration and knowledge discovery using biological networks
| Τίτλος: | Data integration and knowledge discovery using biological networks |
|---|---|
| Συγγραφείς: | Gaurav Kumar |
| Έτος έκδοσης: | 2013 |
| Θεματικοί όροι: | Other education not elsewhere classified, protein protein interaction, metabolite linked protein interaction, Genes, network biology, gene expression, Proteins -- Analysis -- Data processing, data integration, Gene regulatory networks, Computational biology, Protein-protein interaction, gene ontology, Proteins, Genes -- Analysis -- Data processing, Biology, Bioinformatics, Biology -- Information resources |
| Περιγραφή: | The overall objective of this thesis is to analyse and understand the intricate network of protein interactions inside the cell. Proteins are molecular machines, which interact and communicate to perform different cellular functions. Research effort in molecular and cellular biology enables the detection of molecular interactions on a large scale. The experimental results generated by high-throughput studies are archived in various public databases. In this study, statistical and computational approach is used to integrate information from relative inhomogeneous data sources (public databases) derived from high-throughput experiments. Further, the integrated approach is used to explore the relationships within the interacting protein pairs. Graph-based network model is used to determine the protein relationships based on gene ontology (GO) biological processes, molecular functions and cellular components. -- Network approach has enabled researchers to study the pervasive nature of protein interactions in systems biology. Moreover, different computational methods have been developed to analyse networks and their topological properties. Foremost among them are the methods for analysing direct/indirect protein interactions networks by integrating with the other types of -omic data. This thesis demonstrates the statistical significance of protein interaction networks for the study of subcellular localisation, biological processes and molecular functions. It also suggests the significance of network in biological studies. -- The protein-protein interaction (PPIs) network was created by integrating binary protein interactions deposited in various public databases. Similarly, the metabolic network was created by linking proteins via metabolites, i.e. indirect protein interactions. Both PPIs and metabolic networks were analysed to show the difference in network topologies. Further, we compared and contrasted the subcellular localisation of human proteins using PPIs and metabolic networks. The statistical significance of ... |
| Τύπος εγγράφου: | thesis |
| Γλώσσα: | unknown |
| Relation: | https://figshare.com/articles/thesis/Data_integration_and_knowledge_discovery_using_biological_networks/19431188 |
| DOI: | 10.25949/19431188.v1 |
| Διαθεσιμότητα: | https://doi.org/10.25949/19431188.v1 https://figshare.com/articles/thesis/Data_integration_and_knowledge_discovery_using_biological_networks/19431188 |
| Rights: | In Copyright |
| Αριθμός Καταχώρησης: | edsbas.C9111A14 |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.25949/19431188.v1 |
|---|