Electrical and Electronic Engineering - Research Publications

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Type: Conference Paper × Author: Palaniswami, Marimuthu × Author: SHILTON, ALISTAIR × Author: GUBBI LAKSHMINARASIMHA, JAYAVARDHANA ×

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    Real value solvent accessibility prediction using adaptive support vector regression
    Gubbi, J ; Shilton, A ; Palaniswami, M ; Parker, M (IEEE, 2007)
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    Protein topology classification using two-stage support vector machines.
    Gubbi, J ; Shilton, A ; Parker, M ; Palaniswami, M (Universal Academy Press, 2006)
    The determination of the first 3-D model of a protein from its sequence alone is a non-trivial problem. The first 3-D model is the key to the molecular replacement method of solving phase problem in x-ray crystallography. If the sequence identity is more than 30%, homology modelling can be used to determine the correct topology (as defined by CATH) or fold (as defined by SCOP). If the sequence identity is less than 25%, however, the task is very challenging. In this paper we address the topology classification of proteins with sequence identity of less than 25%. The input information to the system is amino acid sequence, the predicted secondary structure and the predicted real value relative solvent accessibility. A two stage support vector machine (SVM) approach is proposed for classifying the sequences to three different structural classes (alpha, beta, alpha+beta) in the first stage and 39 topologies in the second stage. The method is evaluated using a newly curated dataset from CATH with maximum pairwise sequence identity less than 25%. An impressive overall accuracy of 87.44% and 83.15% is reported for class and topology prediction, respectively. In the class prediction stage, a sensitivity of 0.77 and a specificity of 0.91 is obtained. Data file, SVM implementation (SVMHEAVY) and result files can be downloaded from http://www.ee.unimelb.edu.au/ISSNIP/downloads/.
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    Disulphide Bridge Prediction using Fuzzy Support Vector Machines
    Jayavardhana, Rama G. L. ; SHILTON, ALISTAIR ; PARKER, MICHAEL ; PALANISWAMI, MARIMUTHU ( 2005)
    One of the major contributors to the native form of protien is cystines forming covalent bonds in oxidized state. The Prediction of such bridges from the sequence is a very challenging task given that the number of bridges will rise exponentially as the number of cystines increases. We propose a novel technique for disulphide bridge prediction based on Fuzzy Support Vector Machines. We call the system DIzzy. In our investigation, we look at disulphide bond connectivity given two Cystines with and without a priori knowledge of the bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features such as the probability of occurrence of each amino acid in different secondary structure states along with psiblast profiles. The performance is compared with normal support vector machines. We evaluate our method and compare it with the existing method using SPX dataset.