2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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 ... View full abstract»

Protein structure prediction has been a very important and challenging research problem in bioinformatics for years. Yet the determination of protein structures by time-consuming and relatively expensive experimental methods continues to lag far behind the explosive discovery of protein sequences. With the recent breakthrough of combining multiple sequence alignment information and artificial inte... View full abstract»

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... View full abstract»

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 tha... View full abstract»

An exploration of the use of multiple secondary structure elements for structural RNA gene finding is conducted. The secondary structure models are combined through a multilayer voting system which first combines the probability output of support vector machines and then combines the results of those votes to predict whether a sequence is a structural RNA gene or not. It is found that the voting i... View full abstract»

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 autocorrela... View full abstract»

This paper outlines the use of Artificial Immune Recognition System (AIRS) within the field of text/document classification. Various versions of AIRS including AIRS1, AIRS2, Parallel AIRS and Modified AIRS with Fuzzy KNN are applied to classify the mode of a text's content which is organized for helping users with their organizational tasks. In this regard, 7 major features as inputs with 3 nomina... View full abstract»

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 ... View full abstract»

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 metho... View full abstract»

This study constructs and tests an agent-based model of bacterial communities with the goal of modeling the observation that the majority of bacteria in nature cannot be cultured. The new field of metagenomics, the direct, mass sequencing of DNA recovered from the environment, is the source of this observation. The hypothesis tested is that bacteria form interdependent communities so that viable l... View full abstract»

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... View full abstract»

Epidemic models often incorporate contact networks along which the disease can be passed. This study incorporates a restarting-recentering evolutionary algorithm, previously developed to locate extremal epidemic networks, together with a new representation, the toggle-delete representation, for evolvable networks. The goal is to locate networks that were likely to have produced a given epidemic be... View full abstract»

Previous studies show that Gibbs sampling methods and the like desperately failed to solve the challenging motif problem. This paper proposes a new hybrid algorithm, integrated Gibbs with particle swarm optimization (PSO) based motif alignment clustering (PSO-MAC), to solve the challenging motif problem by iteratively refining a population of potential solutions. The PSO-MAC algorithm is closely i... View full abstract»

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 ... View full abstract»

In this paper we investigate two types of artificial organism which have the potential to be useful in biological simulations at the genomic level, such as simulations of speciation or gene interaction. Biological problems of this type are usually studied either with simulations using artificial genes that are merely evolving strings with no phenotype, ignoring the possibly crucial contribution of... View full abstract»

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 suc... View full abstract»

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... View full abstract»

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 research. However, for gene order data, traditional bootstrap procedures can not be applied because gene order data is viewed as one character with various states.... View full abstract»

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 selec... View full abstract»

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 transp... View full abstract»

The increasing size and complexity of modern data sets turns modern data mining techniques to indispensable tools when inspecting biomedical data sets. Thereby, dedicated data formats and detailed information often cause the need for problem specific similarities or dissimilarities instead of the standard Euclidean norm. Therefore, a number of clustering techniques which rely on similarities or di... View full abstract»