BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:A new microRNA target prediction tool identifies a novel interacti
 on of a putative miRNA with CCND2
DTSTART;VALUE=DATE-TIME:20121017T093000Z
DTEND;VALUE=DATE-TIME:20121017T100000Z
DTSTAMP;VALUE=DATE-TIME:20261011T083007Z
UID:indico-contribution-165@indico.ipb.ac.rs
DESCRIPTION:Speakers: Anastasis Oulas (IMBG-HCMR)\nComputational methods f
 or miRNA target prediction vary in the algorithm used\; and while one can 
 state opinions about the strengths or weaknesses of each particular algori
 thm\, the fact of the matter is that they fall substantially short of capt
 uring the full detail of physical\, temporal\, and spatial requirements of
  miRNA::target-mRNA interactions.  Here\, we introduce a novel miRNA targe
 t prediction tool called Targetprofiler that utilizes a probabilistic lear
 ning algorithm in the form of a hidden Markov model trained on experimenta
 lly verified miRNA targets. Using a large scale protein down-regulation da
 taset we validate our method and compare its performance to existing tools
 . We find that Targetprofiler exhibits greater correlation between computa
 tional predictions and protein down-regulation and predicts experimentally
  verified miRNA targets more accurately than 3 other tools. Concurrently\,
  we use primer extension to identify the mature sequence of a novel miRNA 
 gene recently identified within a cancer associated genomic region and use
  Targetprofiler to predict its potential targets. Experimental verificatio
 n of the ability of this small RNA molecule to regulate the expression of 
 CCND2\, a gene with documented oncogenic activity\, confirms its functiona
 l role as a miRNA. These findings highlight the competitive advantage of o
 ur tool and its efficacy in extracting bio-logically significant results.\
 n\nhttps://events.saifa.rs/event/291/contributions/165/
LOCATION:National Library of Serbia
URL:https://events.saifa.rs/event/291/contributions/165/
END:VEVENT
END:VCALENDAR