Associating one or more Gene Ontology (GO) terms to a protein means making a statement about a particular functional characteristic of the protein. This association provides scientists with a snapshot of the biological context of the protein activity. This paper introduces PRONTO-TK, a Python-based software toolkit designed to democratize access to Neural-Network based complex protein function prediction workflows. PRONTO-TK is a user-friendly graphical interface (GUI) for empowering researchers, even those with minimal programming experience, to leverage state-of-the-art Deep Learning architectures for protein function annotation using GO terms. We demonstrate PRONTO-TK's effectiveness on a running example, by showing how its intuitive configuration allows it to easily generate complex analyses while avoiding the complexities of building such a pipeline from scratch.

PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction / Politano, Gianfranco; Benso, Alfredo; Rehman, Hafeez Ur; Re, Angela. - In: NAR GENOMICS AND BIOINFORMATICS. - ISSN 2631-9268. - 6:3(2024). [10.1093/nargab/lqae112]

PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction

Politano, Gianfranco;Benso, Alfredo;Re, Angela
2024

Abstract

Associating one or more Gene Ontology (GO) terms to a protein means making a statement about a particular functional characteristic of the protein. This association provides scientists with a snapshot of the biological context of the protein activity. This paper introduces PRONTO-TK, a Python-based software toolkit designed to democratize access to Neural-Network based complex protein function prediction workflows. PRONTO-TK is a user-friendly graphical interface (GUI) for empowering researchers, even those with minimal programming experience, to leverage state-of-the-art Deep Learning architectures for protein function annotation using GO terms. We demonstrate PRONTO-TK's effectiveness on a running example, by showing how its intuitive configuration allows it to easily generate complex analyses while avoiding the complexities of building such a pipeline from scratch.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2992102