.png){kind=icon}
.png){kind=icon}
Return
Bayesian model selection for mining mass spectrometry data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2005.06.046.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to microorganism detection from mass spectrometry data. (c) 2005 Published by Elsevier Ltd.
Keywords:
Bayesian networks
structure learning
domain knowledge
model selection
mass spectrometry
microorganism detection
biomarker
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.3
Papers:
7.8K
Citations:
3.0W
Organization
No organization information available