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UAI
2003
15 years 5 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
CORR
2011
Springer
243views Education» more  CORR 2011»
14 years 11 months ago
Localization from Incomplete Noisy Distance Measurements
—We consider the problem of positioning a cloud of points in the Euclidean space Rd , from noisy measurements of a subset of pairwise distances. This task has applications in var...
Adel Javanmard, Andrea Montanari
ICML
2005
IEEE
16 years 5 months ago
Predicting protein folds with structural repeats using a chain graph model
Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeat...
Yan Liu, Eric P. Xing, Jaime G. Carbonell
BMCBI
2011
14 years 8 months ago
RegNetB: Predicting Relevant Regulator-Gene Relationships in Localized Prostate Tumor Samples
Background: A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is dif...
Angel Alvarez, Peter J. Woolf
BMCBI
2007
136views more  BMCBI 2007»
15 years 4 months ago
Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees
Background: In vertebrates, a large part of gene transcriptional regulation is operated by cisregulatory modules. These modules are believed to be regulating much of the tissue-sp...
Xiaoyu Chen, Mathieu Blanchette