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Welcome to the 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management organized by the IEEE Computational Intelligence Society. The workshop will provide the attendees a valuable opportunity to meet professional colleagues on a personal level. The topics to be covered reflect the depth and breadth of the current research effort and range of applications through presentations,...
MicroRNAs (miRNAs) are small non-coding RNAs that have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNAs often results in the development of cancer. In this article, we have utilized a multiobjective genetic algorithm-based feature selection algorithm wrapped with support vector machine (SVM) classifier for selecting...
The use of covariance models in finding non-coding RNA gene members in genome sequence databases has been shown quite effective in many studies. However, it has a significant drawback, which is the very large computational burden. A combined covariance model is proposed to reduce the search complexity when a genome sequence is searched for more than one ncRNA gene family. The covariance models that...
NCBI has been accumulating a large repository of microarray data sets, namely Gene Expression Omnibus (GEO). GEO is a great resource enabling one to pursue various biological and pathological questions. The question we ask here is: given a set of gene signatures and a classifier, what is the best minimum sample size in a clinical microarray research that can effectively distinguish different types...
Gene order changes under rearrangement events such as inversions and transpositions have attracted increasing attention as a new type of data for phylogenetic analysis. Since these events are rare, they allow the reconstruction of evolutionary history far back in time. Many software have been developed for the inference of gene order phylogenies, including widely used maximum parsimony methods such...
Digital imaging is nowadays widely employed in the field of optical microscopy. One of the most apparent benefits consists in the possibility for the researcher to see the whole biological sample in one image, achieved by collecting all the parts being inspected. Common approaches work in batch mode and rely on known motorized x–y stage offsets of the microscope holder. Or alternatively, the methods...
The purpose of using resampling methods on phylogenetic data is to estimate the confidence value of branches. In recent years, bootstrapping and jackknifing are the two most popular resampling schemes which are widely used in biological reserach. However, for gene order data, traditional bootstrap procedures can not be applied because gene order data is viewed as one character with various states...
Automated microscopic image analysis techniques are increasingly gaining attention in the field of biological imaging. The success of these applications mostly depends on the earlier image processing steps applied to the acquired images, aiming at enhancing image content while performing noise and artifacts removal. One such artifact is the vignetting effect that in general occurs in most imaging...
Several machine learning techniques were evaluated for the prediction of logP. The algorithms used include artificial neural networks (ANN), support vector machines (SVM) with the extension for regression, and kappa nearest neighbor (k-NN). Molecules were described using optimized feature sets derived from a series of scalar, two- and three-dimensional descriptors including 2-D and 3-D autocorrelation,...
In the last decade many computational approaches have been introduced to model networks of molecular interactions from gene expression data. Such networks can provide an understanding of the regulatory mechanisms in the cells. System identification algorithms refer to a group of approaches that capture the dynamic relationship between the input and output of a system, and provide a deterministic model...
Identifying the protein coding regions in the DNA sequence is an active issue in computational biology. Presently, there are many outstanding methods in predicting the coding regions with extreme high accuracy, after conducting preceding training process. However, the training dependence may reduce adaptability of the methods, particularly for new sequences from unknown organisms with no or small...
This project presents two methods for image classification for the detection of malignant melanoma: the Mahalanobis-Taguchi System and Finite State Classifiers. The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases, while Finite State Classifiers are a state based machine learning technique. The goal of this study is to compare the ability...
DNA error correcting codes over the edit metric can be used to correct sequencing errors. The codewords may be used as embeddable markers that allow one to track the origin of sequence data. The Salmon Algorithm is a search meta-heuristic inspired by the behaviour of salmon swimming upstream to spawn. This algorithm consists of a number of parameters, which we tune for the purpose of constructing...
Semi-supervised clustering incorporating biological relevance as a prior knowledge has been favored over the past decade. However, selection of prior knowledge has been a challenge. We generate prior knowledge from Gene Ontology (GO) terms at different levels of GO hierarchy and use them to study their impact on the performance of subsequent clustering of microarray data by using MPCKMeans and GOFuzzy...
In this paper, a generalized operator based nonlinear fuzzy clustering model is proposed. Target data of this model is similarity data and the obtained similarity data has various structures. Therefore, for general-purpose, the generalized operators are defined on a product space of linear spaces in order to consider the variety of the structures of similarity between a pair of objects by revising...
Although mostly used for pattern classification, linear discriminant analysis (LDA) may also be used for feature selection. When employed to select genes for microarray data, which has high dimensionality and small sample size, LDA encounters three problems, including singularity of scatter matrix, overfitting and prohibitive computational complexity. In this study, we propose a new regularization...
This study introduces the novel application of a fuzzy network concept to derive optimal margins for use in the treatment of cancer using external beam radiotherapy. The input data for the model is based on the effects of treatment errors, in terms of delineation, organ motion and patient set-up errors, on tumour coverage and doses to critical organs. A demonstrable improvement in the model transparency...
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