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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2992102