TY - JOUR
T1 - Protein structural class determination using support vector machines
AU - Isik, Zerrin
AU - Yanikoglu, Benin
AU - Sezerman, Ugur
PY - 2004
Y1 - 2004
N2 - Proteins can be classified into four structural classes (all-α, all-β, α/β, α+β) according to their secondary structure composition. In this paper, we predict the structural class of a protein from its Amino Acid Composition (AAC) using Support Vector Machines (SVM). A protein can be represented by a 20 dimensional vector according to its AAC. In addition to the AAC, we have used another feature set, called the Trio Amino Acid Composition (Trio AAC) which takes into account the amino acid neighborhood information. We have tried both of these features, the AAC and the Trio AAC, in each case using a SVM as the classification tool, in predicting the structural class of a protein. According to the Jackknife test results, Trio AAC feature set shows better classification performance than the AAC feature. © Springer-Verlag 2004.
AB - Proteins can be classified into four structural classes (all-α, all-β, α/β, α+β) according to their secondary structure composition. In this paper, we predict the structural class of a protein from its Amino Acid Composition (AAC) using Support Vector Machines (SVM). A protein can be represented by a 20 dimensional vector according to its AAC. In addition to the AAC, we have used another feature set, called the Trio Amino Acid Composition (Trio AAC) which takes into account the amino acid neighborhood information. We have tried both of these features, the AAC and the Trio AAC, in each case using a SVM as the classification tool, in predicting the structural class of a protein. According to the Jackknife test results, Trio AAC feature set shows better classification performance than the AAC feature. © Springer-Verlag 2004.
UR - http://www.scopus.com/inward/record.url?scp=33745086645&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-30182-0_9
DO - 10.1007/978-3-540-30182-0_9
M3 - Article
SN - 0302-9743
VL - 3280
SP - 82
EP - 89
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -