Clustering of database sequences for fast homology search using upper bounds on alignment score.

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UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches (2014, background): “…searches against protein sequence clusters have been shown to be equally sensitive but return faster results (Park et al., 2000; Li et al., 2002; Itoh et al., 2004; Cameron et al., 2007) when compared against native sequence databases of sizes ranging from approximately 400 000 to 2.6 million…”
A conditional neural fields model for protein threading (2012, unknown): “1 exp( ( , , , )) E a u a v S T”
Clustered Sequence Representation for Fast Homology Search (2007, unknown): “This is the approach taken by several existing clustering algorithms, including d2_cluster (Burke et al., 1999), OWL (Bleasby and Wootton, 1990), and KIND (Kallberg and Persson, 1999) and Itoh et al. (2004).”