BTR: a bioinformatics tool recommendation system.
Bioinformatics
; 40(5)2024 May 02.
Article
em En
| MEDLINE
| ID: mdl-38662583
ABSTRACT
MOTIVATION The rapid expansion of Bioinformatics research has led to a proliferation of computational tools for scientific analysis pipelines. However, constructing these pipelines is a demanding task, requiring extensive domain knowledge and careful consideration. As the Bioinformatics landscape evolves, researchers, both novice and expert, may feel overwhelmed in unfamiliar fields, potentially leading to the selection of unsuitable tools during workflow development. RESULTS:
In this article, we introduce the Bioinformatics Tool Recommendation system (BTR), a deep learning model designed to recommend suitable tools for a given workflow-in-progress. BTR leverages recent advances in graph neural network technology, representing the workflow as a graph to capture essential context. Natural language processing techniques enhance tool recommendations by analyzing associated tool descriptions. Experiments demonstrate that BTR outperforms the existing Galaxy tool recommendation system, showcasing its potential to streamline scientific workflow construction. AVAILABILITY AND IMPLEMENTATION The Python source code is available at https//github.com/ryangreenj/bioinformatics_tool_recommendation.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Biologia Computacional
/
Fluxo de Trabalho
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos