Recurrent Neural Network for Gene Regulation Network Construction on Time Series Expression Data We propose a new way of exploring potential transcription factor targets in which the Recurrent Neural Network (RNN) is used to model time series gene expression data. Once the training of the RNN is completed, inference is performed through feeding the RNN artificially constructed signals. These artificial signals emulate the original gene expression data and the transcriptional factor of interest is set to be zero constantly to model the knockout state of the transcription factor. The predicted expression patterns of the other genes from the RNN are then used to measure the likelihood that the gene is regulated by the knocked out transcriptional factor. After repeating the same process for each gene as Transcription Factor in the dataset, we construct a gene regulation network with edge weights assigned. We demonstrate the effectiveness of our model by comparing our method with existing popular approaches. The result shows that our RNN method can identify transcription factor targets with higher accuracies than most of existing approaches. Overall, our RNN model trained on time series gene expression data can be useful for discovering transcription factor targets as well as building a gene regulation network.

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.