Publicly-available data sets provide detailed and large-scale information on multiple types of molecular interaction networks in a number of model organisms. These multi-modal univ...
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
We introduce a network-based problem detection framework for distributed systems, which includes a data-mining method for discovering dynamic dependencies among distributed servic...