Electrical and Electronic Engineering - Research Publications

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Author: Palaniswami, Marimuthu × Author: SHILTON, ALISTAIR × Start date: 2000 × End date: 2007 × Type: Journal Article ×

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    Prediction of cystine connectivity using SVM
    Rama, JGL ; Shilton, AP ; Parker, MM ; Palaniswami, M (BIOMEDICAL INFORMATICS, 2005)
    One of the major contributors to protein structures is the formation of disulphide bonds between selected pairs of cysteines at oxidized state. Prediction of such disulphide bridges from sequence is challenging given that the possible combination of cysteine pairs as the number of cysteines increases in a protein. Here, we describe a SVM (support vector machine) model for the prediction of cystine connectivity in a protein sequence with and without a priori knowledge on their bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features (probability of occurrence of each amino acid residue in different secondary structure states along with PSI-blast profiles). We evaluate our method in SPX (an extended dataset of SP39 (swiss-prot 39) and SP41 (swiss-prot 41) with known disulphide information from PDB) dataset and compare our results with the recursive neural network model described for the same dataset.
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    Incremental training of support vector machines
    Shilton, A ; Palaniswami, M ; Ralph, D ; Tsoi, AC (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2005-01)
    We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.