Catálogo de publicaciones - libros

This study investigated the involvement of ventricular focal activity and dispersion of repolarization in LQT2 models at rapid rates. The Luo-Rudy dynamic model was used to simulate ventricular tissues. LQT2 syndrome due to genetic mutations was modeled by modifying the conductances of delayed rectifier potassium currents. Cellular automata was employed to generate virtual tissues coupled with midmyocardial (M) cell clusters. Simulations were conducted using grid-based computation. Under LQT2 conditions, early after-depolarizations (EADs) occurred first at the border of the M refractory zone in epicardium coupled with M clusters, but spiked off from endocardial cells in endocardium coupled with M clusters. The waveform of EADs was affected by the topological distribution of M clusters. Our results explain why subepicardial and subendocardial cells could exhibit surprisingly EADs when adjacent to M cells and suggest that phase 2 EADs are responsible for the onset of Torsade de Pointes at rapid ventricular pacing.

The use of sequence alignments for establishing protein homology relationships has an extensive tradition in the field of bioinformatics, and there is an increasing desire for more statistical methods in the data analysis. We present statistical methods and algorithms that are useful when the protein alignments can be divided into two or more populations based on known features or traits. The algorithms are considered valuable for discovering differences between populations at a molecular level. The approach is illustrated with examples from real biological data sets, and we present experimental results in applying our work on bacterial populations of , where the populations are defined by optimal growth temperature, .

One way of exploring protein unfolding events associated with the development of Amyloid diseases is through the use of multiple Molecular Dynamics Protein Unfolding Simulations. The analysis of the huge amount of data generated in these simulations is not a trivial task. In the present report, we demonstrate the use of Association Rules applied to the analysis of the variation profiles of the Solvent Accessible Surface Area of the 127 amino-acid residues of the protein Transthyretin, along multiple simulations. This allowed us to identify a set of 28 hydrophobic residues forming a hydrophobic cluster that might be essential in the unfolding and folding processes of Transthyretin.

The combination of classifiers has been proposed as a method to improve the accuracy achieved by a single classifier. In this study, the performances of optimistic and pessimistic ordered weighted averaging operators for protein secondary structure classifiers fusion have been investigated. Each secondary structure classifier outputs a unique structure for each input residue. We used confusion matrix of each secondary structure classifier as a general reusable pattern for converting this unique label to measurement level. The results of optimistic and pessimistic OWA operators have been compared with majority voting and five common classifiers used in the fusion process. Using a benchmark set from the EVA server, the results showed a significant improvement in the average Q3 prediction accuracy up to 1.69% toward the best classifier results.

The use of Genetic Algorithms in a 2D Hydrophobic-Hydrophilic (HP) model in protein folding prediction application requires frequent fitness function computations. While the fitness computation is linear, the overhead incurred is significant with respect to the protein folding prediction problem. Any reduction in the computational cost will therefore assist in more efficiently searching the enormous solution space for protein folding prediction. This paper proposes a novel pruning strategy that exploits the inherent properties of the HP model and guarantee reduction of the computational complexity during an ordered traversal of the amino acid chain sequences for fitness computation, truncating the sequence by at least one residue.

In biological problems such as protein sequence family identification and profile building the additive hypothesis of the probability measure is not well suited for modeling HMM based profiles because of a high degree of interdependency among homologous sequences of the same family . Fuzzy measure theory which is an extension of the classical additive theory is obtained by replacing the additive requirement of classical measures with weaker properties of monotonicity, continuity and semi-continuity. The strong correlations and the sequence preference involved in the protein structures make fuzzy measure architecture based models as suitable candidates for building profiles of a given family since fuzzy measures can handle uncertainties better than classical methods . In this paper we investigate the different measures(S-decomposable, and belief measures) of fuzzy measure theory for building profile models of protein sequence problems. The proposed fuzzy measure models have been tested on globin and kinase families . The results obtained from the fuzzy measure models establish the superiority of fuzzy measure theory compared to classical probability measures for biological sequence problems.

The large number of genes in microarray data makes feature selection techniques more crucial than ever. From various ranking-based filter procedures to classifier-based wrapper techniques, many studies have devised their own flavor of feature selection techniques. Only a handful of the studies delved into the effect of redundancy in the predictor set on classification accuracy, and even fewer on the effect of varying the importance between relevance and redundancy. We present a filter-based feature selection technique which incorporates the three elements of relevance, redundancy and differential prioritization. With the aid of differential prioritization, our feature selection technique is capable of achieving better accuracies than those of previous studies, while using fewer genes in the predictor set. At the same time, the pitfalls of over-optimistic estimates of accuracy are avoided through the use of a more realistic evaluation procedure than the internal leave-one-out-cross-validation.

Experiments in DNA microarray provide information of thousands of genes, and bioinformatics researchers have analyzed them with various machine learning techniques to diagnose diseases. Recently Support Vector Machines (SVM) have been demonstrated as an effective tool in analyzing microarray data. Previous work involving SVM used every gene in the microarray to classify normal and malignant lymphoid tissue. This paper shows that, using gene selection techniques that selected only 10% of the genes in “Lymphochip” (a DNA microarray developed at Stanford University School of Medicine), a classification accuracy of about 98% is achieved which is a comparable performance to using every gene. This paper thus demonstrates the usefulness of feature selection techniques in conjunction with SVM to improve its performance in analyzing Lymphochip microarray data. The improved performance was evident in terms of better accuracy, ROC (receiver operating characteristics) analysis and faster training. Using the subsets of Lymphochip, this paper then compared the performance of SVM against two other well-known classifiers: multi-layer perceptron (MLP) and linear discriminant analysis (LDA). Experimental results show that SVM outperforms the other two classifiers.

The availability of microarray technologies has enabled biomedical researchers to explore expression levels of a complete genome simultaneously. The analysis of gene expression patterns can explain the biological basis of several pathological processes. Deepening in the understanding of the molecular processes underlying colorectal cancer might become of interest for the advance of its clinical management. This work presents the analysis of microarrays data using colon cancer samples in order to determine the differentially expressed genes underlying this disease process. The comparison of gene expression levels using a complete genome approach of tumor samples versus healthy controls allows the definition of a set of genes involved in the differentiation of both tissues. The analysis of these differentially expressed genes using Gene Ontology analysis permits the location of most prevalent processes that are altered during under this disease.