Fast and interpretable genomic data analysis using multiple approximate kernel learning

HIGHLIGHTS

• who: Bioinformatics, and ISCB/ISMB, from the Graduate School of Sciences and Engineering, Kocu0327 University, Istanbul, Turkey have published the paper: Fast and interpretable genomic data analysis using multiple approximate kernel learning, in the Journal: (JOURNAL) of October/29,/2021
• what: The authors introduced a scalable MAKL, which is designed specifically for large-scale genomic datasets.
• how: To overcome this problem the authors proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.