superfamily

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superfamily

 [soo´per-fam″ĭ-le]
1. a taxonomic category sometimes established, subordinate to an order and superior to a family.
2. any of a group of proteins having similarities such as areas of structural homology and believed to descend from the same ancestral gene, such as the superfamily of immunoglobulins.

superfamily

/su·per·fam·i·ly/ (soo´per-fam″ĭ-le)
1. a taxonomic category between an order and a family.
2. any of a group of proteins having similarities such as areas of structural homology and believed to descend from the same ancestral gene.

superfamily

(so͞o′pər-făm′ə-lē)
n. pl. superfami·lies
A taxonomic category of related organisms ranking below an order or its subdivisions and above a family.

superfamily

a taxon above a family and below an order.

superfamily

a group of proteins related in structure but different in function, e.g. immunoglobulin superfamily which includes immunoglobulins, T cell receptor, major histocompatibility class I and II, β2-microglobulin, α/β glycoprotein, neuronal cell-adhesive molecule (N-CAM) and neurocytoplasmic protein 3 (NP3).
References in periodicals archive ?
Table 1 shows the superfamilies that were chosen for testing.
The topi accuracy reached when the 'Alpha helix propensity' is used to predict superfamilies is also the highest in the experiments.
In the hierarchical-MCS method, we kept all the proteins of some superfamilies for testing and we trained the classifiers with the remaining proteins.
Then, a multi-class SCOP class predictor is obtained by using the same steps that we used in the single-MCS method but considering SCOP classes instead of SCOP superfamilies.
Even though we predicted the SCOP class for all the proteins in some superfamilies, we only took proteins in one family for testing the superfamily prediction accuracy of the hierarchicalMCS method.
In the hierarchical-MCS method, a classifier is trained for the superfamilies that are inside each SCOP class.
55 data sets, such as the families in the superfamilies 2.
The results suggest that having binary classifiers with high ROC scores allows identifying the correct superfamilies in the test set.