iDHS-DPPE: a method based on dual-path parallel ensemble decision for DNase I hypersensitive sites prediction

Abstract

The DNase I Hypersensitive site (DHS) is the chromatin region that exhibits a hypersensitive response to cleavage by the DNase I enzyme. It is a universal marker for regulatory DNA and associated with genetic variation in a wide range of diseases and phenotypic traits. However, traditional experimental methods have limited the rapid detection of DHS as well as its development. Therefore, effective and accurate methods to explore potential DHSs need to be developed urgently. In this task, a deep learning approach called iDHS-DPPE to predict DHSs in different cell types and developmental stages of the mouse. iDHS-DPPE uses a dual-path parallel integrated neural network to identify DHSs accurately. First, the DNA sequence is segmented into 2-mers to extract information. Then, the DHSs accurately-attention model captures remote dependencies and the MSFRN model enables hierarchical information fusion. The dual models are trained separately to enhance the feature information. Finally, the ensemble decision of two models yields the prediction results, enabling the integration of information from multiple views. The average AUC across all datasets was 93.1% and 93.3% in the 5-fold cross-validation and independent testing experiments, respectively. The experimental results demonstrate that iDHS-DPPE outperforms the state-of-the-art method on all datasets, proving that iDHS-DPPE is effective and reliable for identifying DHSs.