Electrical and Electronic Engineering - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 18
-
ItemJeeva: Enterprise Grid Enabled Web Portal for Protein Secondary Structure Prediction(IEEE, 2008)This paper presents a Grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise Grid technology. The portal is used by research scientists to discover new prediction structures in a parallel manner. An SVM (Support Vector Machine)-based prediction algorithm is used with 64 sample protein sequences as a case study to demonstrate the potential of enterprise Grids.
-
Item
-
ItemProtein topology classification using two-stage support vector machines.(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/.
-
ItemStability Analysis of the Decomposition Method for solving Support Vector Machines( 2005)In situations where processing memory is limited, the Support Vector Machine quadratic program can be decomposed into smaller sub-problems and solved sequentially. The convergence of this method has been proven previously through the use of a counting method. In this initial investigation, we approach the convergence analysis by treating the decomposed sub-problems as subsystems of a general system. The gradients of the subproblems and the inequality constraints are explicitly modelled as system variables. The change in these variables during optimization form a dynamic system modelled by vector differential equations. We show that the change in the objective function can be written as the energy in the system. This makes it a natural Lyapunov function which has an asymptotically stable point at the origin. The asymptotic stability of the whole system then follows under certain assumptions.
-
ItemDisulphide Bridge Prediction using Fuzzy Support Vector Machines( 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.
-
ItemA convergence rate estimate for the SVM decomposition method( 2005)The training of Support Vector Machines using the decomposition method has one drawback; namely the selection of working sets such that convergence is as fast as possible. It has been shown by Lin that the rate is linear in the worse case under the assumption that all bounded Support Vectors have been determined. The analysis was done based on the change in the objective function and under a SVMlight selection rule. However, the rate estimate given is independent of time and hence gives little indication as to how the linear convergence speed varies during the iteration. In this initial analysis, we provide a treatment of the convergence from a gradient contraction perspective. We propose a necessary and sufficient condition which when satisfied provides strict linear convergence of the algorithm. The condition can also be interpreted as a basic requirement for a sequence of working sets in order to achieve such a convergence rate. Based on this condition, a time dependant rate estimate is then further derived. This estimate is shown to monotonically approach unity from below.
-
Item
-
Item
-
Item
-
Item