Predicting in-Vitro Transcription Factor Binding Sites with Deep Embedding Convolution Network

With the rapid development of deep learning, convolution neural network achieve great success in predicting DNA-transcription factor binding, aka motif discovery, In this paper, we propose a novel neural network based architecture i.e. eDeepCNN, combining multi-layer convolution network and embedding layer for predicting in-vitro DNA protein binding sequence. Our model fully utilize fitting capacity of deep convolution neural network and is well designed to capture the interaction pattern between motifs in neighboring sequence. Meanwhile continuous embedding vector serves as a better description of nucleotides than one-hot image-like representation owing to its superior expressive ability. We verify the effectiveness of our model on 20 motif datasets from in-vitro protein binding microarray data (PBMs) and present promising results compared with well-established DeepBind model. In addition, we emphasis the significance of dropout strategy in our model to fight against the overfitting problem along with growing model complexity.