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DTSTAMP:20260902T232158Z
UID:d12b7482-b90d-4562-acf7-b40c236c7bd7
DTSTART:20240118T090000Z
DTEND:20240118T170000Z
DESCRIPTION:UniProt is a high quality\, comprehensive protein resource in w
 hich the core activity is the expert review and annotation of proteins whe
 re the function has been experimentally investigated. At the same time\, t
 he UniProt database contains large numbers of proteins which are predicted
  to exist from gene models\, but which do not have associated experimental
  evidence indicating their function. UniProt commits significant resources
  to developing computational methods for functional annotation of these pr
 edicted proteins based on the data in entries that have gone through the e
 xpert review process.  \n  \nWe will describe the two main automated annot
 ation systems currently in use. First\, UniRule\, which is an established 
 UniProt system in which curators manually develop rules for annotation. Se
 cond\, ARBA (Association-Rule-Based Annotator)\, which is a multi-class le
 arning system which uses rule mining techniques to generate concise annota
 tion models. ARBA employs a data exclusion algorithm that censors data not
  suitable for computational annotation\, and generates human-readable rule
 s for each UniProt release. As part of our interest in engaging with the m
 achine learning community\, we will also introduce the contribution of Pro
 tNLM (Protein Natural Language Model)\, from Google Research\, which annot
 ates proteins which have "uncharacterised" names.  \n  \nWe will also intr
 oduce UniFIRE\, an open source software that enables researchers to annota
 te their own protein dataset by using the above mentioned annotation syste
 ms. In order to provide an easy and straightforward way to download and se
 t up this tool we have containerised UniFIRE together with all its depende
 ncies and the latest set of UniRule and ARBA rules. In this webinar\, we w
 ill show how to create functional predictions for protein sequences by usi
 ng this container image.
SUMMARY:Automated annotation in UniProt
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/automated-annotation-un
 iprot-2024
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