NCNet: Deep Learning Network Models for Predicting Function of Non-coding DNA - Zhang, Hanyu; Hung, Che-Lun; Liu, Meiyuan; Hu, Xiaoye; Lin, Yi-Yang (Frontiers in Genetics, 2019)
In human genome, it consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, majority of disease-associated variants lie in these regions. To predict function of non-coding DNA is critical. Hence, we propose NCNet, which integrates deep residual learning and sequence to sequence learning network, to predict transcription factor (TF) binding sites, which can then be used to predict non-coding function. In NCNet, deep residual learning is used to identify regulatory patterns of motifs and sequence to sequence learning is used to extract sequential dependency between these patterns. With the identity shortcut technique, NCNet achieves significant improvement compared to previews hybrid framework models for identifying regulatory markers.