D2VCB: A Hybrid Deep Neural Network for the Prediction of invivo ProteinDNA Binding from Combined DNA Sequence Prediction of invivo proteinDNA binding is an important, but challenging task in the broad field of computational biology. Although some methods based on deep learning have succeed in modeling invivo proteinDNA binding, they often simply extract the sequence features from the original DNA sequence without consideration of other sequence features, such as their reverse, complementary and reverse complementary sequences. Also, onehot encoding of DNA sequence is vulnerable to the curse of dimensionality, which leads to unwanted equidistance of pairwise sequences. To address these problems, we propose D2VCB (dna2vec, convolution, biLSTM), a novel hybrid deep neural network framework using dna2vec to predict invivo proteinDNA binding events. We extract input features from DNA original sequences, reverse sequences, complementary and complementary reverse sequences, and then use dna2vec to compute a distributed representation of kmer. In our D2VCB model, the convolution layer captures motif features, while the recurrent layer captures longterm dependencies among motif features so as to improve prediction accuracy. Our performance comparison experiments show that D2VCB outperforms significantly other existing methods in terms of multiple performance metrics.

Discovering proteinbinding RNA motifs with a generative model of RNA sequences Abstract   Recent advances in highthroughput experimental technologies have generated a huge amount of data on interactions between proteins and nucleic acids. Motivated by the big experimental data, several computational methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new nucleic acid sequences that bind to a target protein because they are classifiers rather than generators. In this paper we propose a generative model for constructing proteinbinding RNA sequences and motifs using a long shortterm memory (LSTM) neural network. Testing the model for several target proteins showed that RNA sequences generated by the model have high binding affinity and specificity for their target proteins and that the proteinbinding motifs derived from the generated RNA sequences are comparable to the motifs from experimentally validated proteinbinding RNA sequences. The results are promising and we believe this approach will help design more efficient in vitro or in vivo experiments by suggesting potential RNA aptamers for a target protein.