Exposing and Characterizing Subpopulations of Distinctly Regulated Genes by KPlane Regression Understanding the roles and interplays of histone marks and transcription factors in the regulation of gene expression is of great interest in the development of noninvasive and personalized therapies. Computational studies at genomewide scale represent a powerful explorative framework, allowing to draw general conclusions. However, a genomewide approach only identifies generic regulative motifs, and possible multifunctional or coregulative interactions may remain concealed. In this work, we hypothesize the presence of a number of distinct subpopulations of transcriptional regulative patterns within the set of protein coding genes that explain the statistical redundancy observed at a genomewide level. We propose the application of a KPlane Regression algorithm to partition the set of protein coding genes into clusters with specific shared regulative mechanisms. Our approach is completely datadriven and computes clusters of genes significantly better fitted by specific linear models, in contrast to single regressions. These clusters are characterized by distinct and sharper histonic input patterns, and different mean expression values.

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.