Tool recommender system in Galaxy using deep learning.

ABSTRACT:

Background

Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands of tools, especially for researchers new to Galaxy. To help researchers with creating workflows, a system is developed to recommend tools that can facilitate further data analysis.

Findings

A model is developed to recommend tools using a deep learning approach by analysing workflows composed by researchers on the European Galaxy server. The higher-order dependencies in workflows, represented as directed acyclic graphs, are learned by training a gated recurrent units neural network, a variant of a recurrent neural network. In the

SUBMITTER: Kumar A 

PROVIDER: S-EPMC7786169 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature