BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20260721T101031Z
UID:2ebbc1a3-11b7-4cb4-b138-690820b4d03f
DTSTART:20221208T000000Z
DTEND:20221208T000000Z
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  \n We will describe the two main automated anno
 tation systems currently in use. First\, UniRule\, which is an established
  UniProt system in which curators manually develop rules for annotation. S
 econd\, ARBA (Association-Rule-Based Annotator)\, which is a multi-class l
 earning system which uses rule mining techniques to generate concise annot
 ation models. ARBA employs a data exclusion algorithm that censors data no
 t suitable for computational annotation\, and generates human-readable rul
 es for each UniProt release. As part of our interest in engaging with the 
 machine learning community\, we will also introduce the contribution of Pr
 otNLM (Protein Natural Language Model)\, from Google Research\, which anno
 tates proteins which have "uncharacterised" names.  \n  \n We will also in
 troduce UniFIRE\, an open source software that enables researchers to anno
 tate their own protein dataset by using the above mentioned annotation sys
 tems. In order to provide an easy and straightforward way to download and 
 set up this tool we have containerised UniFIRE together with all its depen
 dencies and the latest set of UniRule and ARBA rules. In this webinar\, we
  will show how to create functional predictions for protein sequences by u
 sing this container image.
SUMMARY:Automated annotation in UniProt
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/automated-annotation-un
 iprot-2022
END:VEVENT
END:VCALENDAR