2 papers
cs.LG2020
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
Xinshi Chen, Yu Li, Ramzan Umarov +2
In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints…
q-bio.GN2018
PromID: human promoter prediction by deep learning
Ramzan Umarov, Hiroyuki Kuwahara, Yu Li +2
Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with…