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Protein Complex Prediction from Protein Interaction Networks - Coggle…
Protein Complex Prediction from Protein Interaction Networks
detecting sparse complexes
k-connectedness
Protein complex prediction based on k-connected subgraphs in protein interaction network
neighborhood density
Identifying Complexes from Protein Interaction Networks According to Different Types of Neighborhood Density
post-processing method
Employing functional interactions for characterisation and detection of sparse complexes from yeast PPI networks
functional interactions
detecting small complexes
probability-based model
Sampling strategy for protein complex prediction using cluster size frequency
Metropolis-Hastings
PPSampler2: Predicting protein complexes more accurately and efficiently by sampling
Metropolis-Hastings
Discovery of small protein complexes from PPI networks with size-specific supervised weighting
Naive Bayes
detecting overlapping complexes
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)
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)
more recent methods
machine learning
Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes
contrast patterns
emerging patterns
explain properties of complexes
predict across organisms