Transformer-based tool recommendation system in Galaxy.

ABSTRACT:

Background

Galaxy is a web-based open-source platform for scientific analyses. Researchers use thousands of high-quality tools and workflows for their respective analyses in Galaxy. Tool recommender system predicts a collection of tools that can be used to extend an analysis. In this work, a tool recommender system is developed by training a transformer on workflows available on Galaxy Europe and its performance is compared to other neural networks such as recurrent, convolutional and dense neural networks.

Results

The transformer neural network achieves two times faster convergence, has significantly lower model usage (model reconstruction and prediction) time and shows a better generalisation that goes beyond training workflows than the older tool recommender system create

SUBMITTER: Kumar A 

PROVIDER: S-EPMC10680333 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature