Using Chou’s 5‑Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature

It
is well known that DNA-protein binding (DPB) prediction is not
only beneficial to understand the regulation mechanism of gene expression
but also a challenging task in the field of computational biology.
Traditional methods for DPB prediction that depend on manually extracted
features may lead to classification errors. Recently, deep learning
such as convolutional neural network (CNN) has been successfully applied to classification tasks and
improved DPB prediction performance significantly. Yet, these methods
are based on the original DNA sequence modeling, ignoring the hidden
complex dependency and complementarity between multiple sequence features.
In consideration of this problem, we propose a method to fuse different
sequence features and analyze them systematically through multi-scale
CNN. First, sliding windows of specified lengths are set on distinct
DNA sequences to generate multiple sequence features with unequal
lengths. Second, multiple feature sequences are fused and encoded
for feature representation. Third, multi-scale CNN with different
binding motif lengths is used to automatically learn and mine the
influence of internal attributes and hidden complex relations between
the fusion sequence features and make full use of the complementary
advantages of extracted CNN features to predict DPB. When our model
is applied to 690 ChIP-seq datasets, it achieves an average AUC of
0.9112, which is significantly better than the latest methods. The
results show that our method is effective for DPB prediction and is
freely available at http://121.5.71.120/mscDPB/.