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The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular functions and medical diagnosis. Neural networks and bayesian methods have performed well on the protein(More)
Linear dimensionality reduction (LDR) techniques are quite important in pattern recognition due to their linear time complexity and simplicity. In this paper, we present a novel LDR technique which, though linear, aims to maximize the Chernoff distance in the transformed space; thus, augmenting the class separability in such a space. We present the(More)
BACKGROUND Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation,(More)
A new linear dimensionality reduction (LDR) technique for pattern classification and machine learning is presented, which, though linear, aims at maximizing the Chernoff distance in the transformed space. The corresponding two-class criterion, which is maximized via a gradient-based algorithm, is presented and initialization procedures are also discussed.(More)
In this paper, we formally present a novel estimation method, referred to as the Stochastic Learning Weak Estimator (SLWE), which yields the estimate of the parameters of a binomial distribution, where the convergence of the estimate is weak, i.e. with regard to the rst and second moments. The estimation is based on the principles of stochastic learning.(More)
Image and statistical analysis are two important stages of cDNA microarrays. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment used to print the arrays,(More)
Optimal multilevel thresholding is a quite important problem in image segmentation and pattern recognition. Although efficient algorithms have been proposed recently, they do not address the issue of irregularly sampled histograms. A polynomial-time algorithm for mul-tilevel thresholding of irregularly sampled histograms is proposed. The algorithm is(More)
Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches(More)