Study of CITE-seq protein expression prediction method based on LSTM

Abstract

CITE-seq consists of mRNA sequencing and labeled antibody sequencing. This paper predicts labeled antibody sequences based on LSTM neural network. Research shows that LSTM algorithm has a reliable application in CITE-seq protein expression prediction technology, and the prediction accuracy rate reaches 95%. The LSTM algorithm proposed in this paper is relatively accurate and can simulate the gene sequence of proteins well. The CITE-seq data training model learns the potential relationship between RNA and protein, realizes the prediction of protein expression by using scRNA-seq data, greatly reduces the cost of CITE-seq test experiment, and improves the experimental efficiency.