Sentence examples for based on probabilistic graphical from inspiring English sources
All of them are based on probabilistic graphical models such as the hidden Markov model (HMM) and dynamic Bayesian network.
Other machine learning and physics approaches are based on probabilistic graphical models such as Latent Dirichlet Allocation (LDA, Airoldi et al., 2008; Ball et al., 2011; Blei et al., 2003).
The third method abandons the biophysical model in favor of machine learning algorithms that empirically infer structure based on probabilistic graphical models [e.g. CONTRAfold (Do et al., 2006)] or non-parametric methods [e.g. KNETfold (Bindewald et al., 2006)].
Based on a list of genes loaded from a file, the user can construct a sub-network, perform network clustering to search for network modules related to patient clinical or other phenotypic information, annotate network modules, perform pathway enrichment analysis, and even model pathway activities based on probabilistic graphical models.
Several network approaches are available, such as the Weighted Gene Analysisssion NetWGCNAAnalysis (WGCNA) method (based on correlation patterns between expression profiles) [ 14] which has proven its superiority over Partial Correlation and Information Theory (PCIT) methods [ 15], and the Lemon-Tree project (based on probabilistic graphical models) [ 16].
There exist several algorithms for unsupervised learning that are based on probabilistic reasoning, such as Bayes networks, graphical models, multiple eigenspaces, and different variants of HMMs.
The first one is based on probabilistic estimation [9].
Will we toss the embryo, "start all over again and try for a better one?" Or change the offending genes based on probabilistic outcomes?
TEMPI uses the observed data (time-course gene expression data, known PPIs and GOBPs) as the input variables, estimates the output variables based on the probabilistic graphical model describing probabilistic dependencies among the input and output variables, and then infers TDNs using the estimated output variables.
In particular, graphical models provide a powerful framework for deriving (links between) numerous existing algorithms based on probabilistic inference [23 25].
