JOURNAL ARTICLE

Compiling Bayesian networks with local structure

Mark ChaviraAdnan Darwiche

Year: 2005 Journal:   Progress in molecular biology and translational science Vol: 195 Pages: 1306-1312   Publisher: Academic Press

Abstract

The endogenous ligands activating a large fraction of the G Protein Coupled Receptor (GPCR) family members have yet to be identified. These receptors are commonly labeled as orphans (oGPCRs), and because of the absence of available pharmacological tools they are currently understudied. Nonetheless, genome wide association studies, together with research using animal models identified many physiological functions regulated by oGPCRs. Similarly, mutations in some oGPCRs have been associated with rare genetic disorders or with an increased risk of developing pathologies. The once underestimated pharmacological potential of targeting oGPCRs is increasingly being exploited by the development of novel tools to understand their biology and by drug discovery endeavors aimed at identifying new modulators of their activity. Here, we summarize recent advancements in the field of oGPCRs and future directions.

Keywords:
Computer science Exploit Inference Bayesian network Compiler Factoring Local area network Bayesian probability Theoretical computer science Compile time Encoding (memory) Artificial intelligence Machine learning Distributed computing Computer network Programming language

Metrics

129
Cited By
12.65
FWCI (Field Weighted Citation Impact)
12
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Bayesian Modeling and Causal Inference
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Graph Neural Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Logic, Reasoning, and Knowledge
Physical Sciences →  Computer Science →  Artificial Intelligence

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