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Supervised machine learning algorithms for protein structure classification
2009
Computational biology and chemistry
We explore automation of protein structural classification using supervised machine learning methods on a set of 11,360 pairs of protein domains (up to 35% sequence identity) consisting of three secondary structure elements. Fifteen algorithms from five categories of supervised algorithms are evaluated for their ability to learn for a pair of protein domains, the deepest common structural level within the SCOP hierarchy, given a one-dimensional representation of the domain structures. This
doi:10.1016/j.compbiolchem.2009.04.004
pmid:19473879
fatcat:3iyyaliujjfhtcwn3i3xfn2n3y