AUTHOR=Fioresi R. , Demurtas P. , Perini G. 
  
TITLE=Deep learning for MYC binding site recognition
  
JOURNAL=Frontiers in Bioinformatics
  
VOLUME=Volume 2 - 2022
  
YEAR=2022
  
URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2022.1015993
  
DOI=10.3389/fbinf.2022.1015993
  
ISSN=2673-7647
  
ABSTRACT=Motivation:
Myc is among the most powerful oncogenes involved in the occurrence and 
development of more than 80\% of different types of pediatric and adult cancers. 
Myc regulates thousands of genes which can be in part different, depending on the 
type of tissues and tumours. % taken into account. 
Myc distribution along the 
genome has been determined experimentally through chromatin immunoprecipitation This approach, although powerful, is very time consuming and cannot be routinely 
applied to tumours of individual patients. Thus, it becomes of paramount 
importance to develop in-silico tools that can effectively and rapidly predict 
its distribution on a given cell genome.
New advanced computational tools (DeeperBind) can then be successfully employed 
to determine the function of Myc in a specific  tumour, and may  help to devise 
new directions and approaches to experiments first and personalized and more 
effective therapeutic treatments for a single patient later on.