Figure 1
From: Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework
An example of a collection of Bayesian network classifiers we learn. The collection consists of several classifiers C1,…,C q , one for each of the q subcellular locations. Directed edges represent dependencies between the connected nodes. There are edges among location variables (L1,…,L q ), as well as between feature variables (F1,…,F d ) and location variables (L1,…,L q ), but not among the feature variables. The latter indicates independencies among features, as well as conditional independencies among features given the locations.