The social life of bioinformatics tools, an OpenEBench's graph-based network
This project presents a graph-based network of the co-usage, understood as being cited by the same scientific publication, of research software from the collection hosted by OpenEBench, and publications from the bioinformatics' domain. The thesis aims to reflect how the bioinformatics' com...
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| Format: | master thesis |
| Publication Date: | 2021 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/356877 |
| Online Access: | https://hdl.handle.net/2117/356877 |
| Access Level: | Open access |
| Keyword: | Databases Bioinformatics Base de dades Base de dades orientada a grafs Neo4j Anàlisi de grafs Base de dades relacional Eines bioinformàtiques Bioinformàtica Xarxa social Benchmarking Flux de treball Database Graph database Analysis of graphs Relational database Bioinformatics' tools Social network Workflows Bases de dades Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
| Summary: | This project presents a graph-based network of the co-usage, understood as being cited by the same scientific publication, of research software from the collection hosted by OpenEBench, and publications from the bioinformatics' domain. The thesis aims to reflect how the bioinformatics' community behave with the tools that they create, identify specific communities in the network and provide guidance on scientific tools worth benchmarking in the context of community-led scientific benchmarking efforts. Also, assess if the noise of the non-significant references relationships in a publication can change the behaviour of the network. To do so, we retrieve more than one million scientific publications related to a subset of bioinformatics tools. Then, we infer tools from the publications with the OpenEBench's platform, enriching them with EDAM ontology terms. After, the relationships among tools and publications are created and upload in a graph database. Finally, a clustering approach, a centrality method and a web page for query the relationships of the tools or topics are applied. To assess the noise, we create a graph for the OpenAccess publications and store the relationships by section (Introduction, Methods, Results, and Discussion). The resulting network is able to show the relationships of any tool or publication and topic of the bioinformatics domain that exists in the graph database. Also, it classifies the network in different communities. The graph of the OpenAccess research papers shows that the noise is not significant enough to change the network's connections and that the relationships retrieved are almost equal to the ones from the Methods section. |
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