SpliceNN: an accurate splice site detector

   October 15, 2021 - Present

   Baltimore, MD

   Center of Computational Biology, Johns Hopkins University

   PhD Student

   In progress

     Mihaele Pertea      Steven Salzberg  

  Introduction

I am developing SpliceNN, a residual-convolutional-neural-network-based deep learning model, aiming to filter out misaligned spliced reads.