Toward a clinical diagnostic pipeline for SPINK1 intronic variants.

Xin-Ying TangJin-Huan LinWen-Bin ZouEmmanuelle MassonArnaud BoullingShun-Jiang DengDavid N CooperZhuan LiaoClaude FérecZhao-Shen LiJian-Min Chen

Published in: Human genomics (2019)

We demonstrated the accuracy and efficiency of in silico prediction in combination with the cell culture-based full-length gene assay for the classification of SPINK1 intronic variants. Based upon these findings, we propose an operational pipeline for classifying SPINK1 intronic variants in the clinical diagnostic setting.

Keyphrases

copy number
machine learning
genome wide
deep learning
gene expression
dna methylation
transcription factor
molecular dynamics simulations
single cell