Gene Regulatory Network Inference with Evolution Strategies and Sparse Matrix Representation Gene Regulatory Networks (GRNs) represent the functional interactions between genes, such as the expression of one gene enhancing the subsequent expression of another gene. GRNs can be inferred from observations of the expressions of various genes over time. This task is difficult, due to the complex interactions among collections of genes, as well as the inherent noise in the observable data. In realistic genomes with thousands of genes, millions of genepairs exist, although the actual number of possible interactions may be considerably smaller, since each gene may affect only a small number of other genes. This motivates the application of sparse learning algorithms for GRN inference. In particular, we explore evolutionary algorithms, and their applications with sparse matrix representations. Our approach can speed up the optimization process and find good solutions, uncovering the underlying GRNs. We study evolution strategies, particle swarm optimization, and a greedy algorithm, and compare their performance in terms of solution quality and computational effort required for GRN inference.

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.