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Table 4 Time complexities of different SVM-based methods for remote homology detection

From: A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

Methods

Time complexities

SVM-Top-n-gram-combine

O (n2l) + O (nml) + O (n2m)

SVM-Bprofile

O (n2l) + O (nml) + O (n2m)

SVM-Ngram

O (nl) + O (nml) + O (n2m)

SVM-Pattern

O (nl lognl+n2l2m) + O (nml) + O (n2m)

SVM-Motif

O (n2l2) + O (nml) + O (n2m)

SVM-Top-n-gram-combine-LSA

O (n2l) + O (nml) + O (nmt) + O (n2R)

SVM-Bprofile-LSA

O (n2l) + O (nml) + O (nmt) + O (n2R)

SVM-Ngram-LSA

O (nl) + O (nml) + O (nmt) + O (n2R)

SVM-Pattern-LSA

O (nl lognl+n2l2m) + O (nml) + O (nmt) + O (n2R)

SVM-Motif-LSA

O (n2l2) + O (nml) + O (nmt) + O (n2R)

SVM-Pairwise

O (n2l2) + O (n3)

SVM-LA

O (n2l2) + O (n3)

Profile

O (n2l) + O (n2l)

SW-PSSM

O (n2l) + O (n2l2)

  1. Where n is the number of training samples, l is the length of the longest training sequence, m is the total number of the words of each building block, t is the minimum of n and m and R is the length of the latent semantic feature vector.
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BMC Bioinformatics

ISSN: 1471-2105

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