Protein Complex Prediction from Protein Interaction Networks
detecting sparse complexes
detecting small complexes
detecting overlapping complexes
more recent methods
k-connectedness
neighborhood density
Protein complex prediction based on k-connected subgraphs in protein interaction network
Identifying Complexes from Protein Interaction Networks According to Different Types of Neighborhood Density
probability-based model
post-processing method
Employing functional interactions for characterisation and detection of sparse complexes from yeast PPI networks
functional interactions
Sampling strategy for protein complex prediction using cluster size frequency
PPSampler2: Predicting protein complexes more accurately and efficiently by sampling
Discovery of small protein complexes from PPI networks with size-specific supervised weighting
Naive Bayes
Metropolis-Hastings
Metropolis-Hastings
machine learning
Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes
contrast patterns
emerging patterns
domain-domain interaction data
Protein complex prediction based on simultaneous protein interaction network
direct separation of competing subgraphs
(Boolean expressions use to encode competing interactions)
explain properties of complexes
predict across organisms
Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions
integer linear programming model
Identifying transcription factor complexes and their roles
greedy combinatorial algorithm (starts with seed)