Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple orga...
Manuel Middendorf, Anshul Kundaje, Chris Wiggins, ...
Abstract. In this study we propose a novel model for the representation of biological networks and provide algorithms for learning model parameters from experimental data. Our appr...
An efficient algorithm is presented for detecting approximate tandem repeats in genomic sequences. The algorithm is based on a flexible statistical model which allows a wide range...
Ydo Wexler, Zohar Yakhini, Yechezkel Kashi, Dan Ge...
We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the successful probabilistic roadmap motion...
Xinyu Tang, Bonnie Kirkpatrick, Shawna L. Thomas, ...
The challenge of similarity search in massive DNA sequence databases has inspired major changes in BLAST-style alignment tools, which accelerate search by inspecting only pairs of...
Phylogenetic hidden Markov models (phylo-HMMs) have recently been proposed as a means for addressing a multispecies version of the ab initio gene prediction problem. These models ...
Transcriptional regulation is mediated by the coordinated binding of transcription factors to the upstream regions of genes. In higher eukaryotes, the binding sites of cooperating...