Improved protein fold assignment using support vector machines.
Because of the relatively large gap of knowledge between number of protein sequences and protein structures, the ability to construct a computational model predicting structure from sequence information has become an important area of research. The knowledge of a protein's structure is crucial in understanding its biological role. In this work, we present a support vector machine based method for recognising a protein's fold from sequence information alone, where this sequence has less similarity with sequences of known structures. We have focused on improving multi-class classification, parameter tuning, descriptor design, and feature selection. The current implementation demonstrates better prediction accuracy than previous similar approaches, and has similar performance when compared with straightforward threading.
Robert E
RE
Langlois
Alice
A
Diec
Ognjen
O
Perisic
Yang
Y
Dai
D000595:Amino Acid Sequence; D011506:Proteins / Q000737:chemistry; D020539:Sequence Analysis, Protein; D012984:Software; D060388:Support Vector Machine
D011506:Proteins