199 citations · 361 across the 7 of their papers we have counts for
7 papers
Motif Discovery through Predictive Modeling of Gene Regulation
Manuel Middendorf, Anshul Kundaje, Mihir Shah +3
We present MEDUSA, an integrative method for learning motif models of transcription factor binding sites by incorporating promoter sequence and gene expression data. We use a moder…
An Information-Theoretic Approach to Network Modularity
Etay Ziv, Manuel Middendorf, Chris Wiggins
Exploiting recent developments in information theory, we propose, illustrate, and validate a principled information-theoretic algorithm for module discovery and resulting measure o…
Predicting Genetic Regulatory Response Using Classification
Manuel Middendorf, Anshul Kundaje, Chris Wiggins +2
We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccha…
Inferring Network Mechanisms: The Drosophila melanogaster Protein Interaction Network
Manuel Middendorf, Etay Ziv, Chris Wiggins
Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific pro…
Predicting Genetic Regulatory Response using Classification: Yeast Stress Response
Manuel Middendorf, Anshul Kundaje, Chris Wiggins +2
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…
Discriminative Topological Features Reveal Biological Network Mechanisms
Manuel Middendorf, Etay Ziv, Carter Adams +6
Recent genomic and bioinformatic advances have motivated the development of numerous random network models purporting to describe graphs of biological, technological, and sociologi…