Background: Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often use...
Matthew Hansen, Logan Everett, Larry Singh, Sridha...
This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while ...
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Abstract. Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present e...
Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. He...