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» Genomic computing networks learn complex POMDPs
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JMLR
2006
118views more  JMLR 2006»
13 years 7 months ago
Learning Factor Graphs in Polynomial Time and Sample Complexity
We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...
Pieter Abbeel, Daphne Koller, Andrew Y. Ng
ESWA
2006
103views more  ESWA 2006»
13 years 7 months ago
Model gene network by semi-fixed Bayesian network
Gene networks describe functional pathways in a given cell or tissue, representing processes such as metabolism, gene expression regulation, and protein or RNA transport. Thus, le...
Tie-Fei Liu, Wing-Kin Sung, Ankush Mittal
RECOMB
2006
Springer
14 years 7 months ago
Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes
Abstract. Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription fa...
Kai Wang, Ilya Nemenman, Nilanjana Banerjee, Adam ...
ESANN
2003
13 years 9 months ago
Neural networks organizations to learn complex robotic functions
Abstract. This paper considers the general problem of function estimation with a modular approach of neural computing. We propose to use functionally independent subnetworks to lea...
Gilles Hermann, Patrice Wira, Jean-Philippe Urban
BMCBI
2008
121views more  BMCBI 2008»
13 years 7 months ago
ReAlignerV: Web-based genomic alignment tool with high specificity and robustness estimated by species-specific insertion sequen
Background: Detecting conserved noncoding sequences (CNSs) across species highlights the functional elements. Alignment procedures combined with computational prediction of transc...
Hisakazu Iwama, Yukio Hori, Kensuke Matsumoto, Koj...