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Found 4 papers, 0 papers with code
Identification of 1H-NMR Spectra of Xyloglucan Oligosaccharides: A Comparative Study of Artificial Neural Networks and Bayesian Classification Using Nonparametric Density Estimation
Proton nuclear magnetic resonance (1H-NMR) is a widely used tool for chemical structural analysis.
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space.
GELATO and SAGE: An Integrated Framework for MS Annotation
The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO).
PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems
When modeling this kind of hierarchical data across large data sets, Bayesian networks become infeasible for representing the probability distributions for the following reasons: i) Each level represents a single random variable with hundreds of thousands of values, ii) The number of levels is usually small, so there are also few random variables, and iii) The structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes, so the network would contain a single linear path for the random variables from each parent to each child node.
