40 research outputs found
Extracción de patrones de comportamiento en datos de expresión genómica
XXVII Jornadas de Automática
6-9 de septiembre de 2006
Universidad de AlmeríaLos algoritmos de biclustering persiguen obtener
subconjuntos de genes que se expresan de una
manera similar frente a un subconjunto de
condiciones. Resulta necesario por tanto poder
determinar la calidad de los biclusters obtenidos.
En este artículo se presenta una técnica basada en
programación lineal para la extracción de patrones
de desplazamiento en biclusters, pudiendo de
esta manera dar una medida de cómo se ajustan
los genes de dichas submatrices a un patrón de
comportamiento. Los resultados obtenidos son
comparados con los que se obtienen utilizando
computación evolutiva
Gestión e implementación de una pasarela HTTP-OPC aplicado a una planta solar
XXVI Jornadas de Automática, Alicante-Elche, 2005El propósito de este artículo es detallar cómo
conseguir un acceso eficiente a datos provenientes
de un dispositivo genérico o planta industrial
situada dentro de una red local. Dicho dispositivo
debe estar conectado a un servidor OPC que
permita acceder a las distintas variables. La
situación que se intenta resolver es aquella en la
que solamente es posible acceder a las variables
de un dispositivo cualquiera desde un equipo que
esté necesariamente situado dentro de la misma
red local. La solución abordada en este artículo
se basa en la elaboración de una aplicación web
que permita proporcionar el acceso a distintos
usuarios tanto desde dentro como desde fuera
de la red a la que pertenezca la planta. Esta
solución permite también el control remoto ya sea
de una planta industrial o cualquier otro tipo de
dispositivo haciendo uso de OPC. En concreto,
se mostrar´a una Planta Solar sobre la que se
explicar´an los aspectos de implementación
Describing the orthology signal in a PPI network at a functional, complex level
In recent work, stable evolutionary signal induced by orthologous proteins has
been observed in a Yeast protein-protein interaction (PPI) network. This finding suggests
more connected subgraphs of a PPI network to be potential mediators of evolutionary information.
Because protein complexes are also likely to be present in such subgraphs, it is
interesting to characterize the bias of the orthology signal on the detection of putative protein
complexes. To this aim, we propose a novel methodology for quantifying the functionality of
the orthology signal in a PPI network at a protein complex level. The methodology performs
a differential analysis between the functions of those complexes detected by clustering a PPI
network using only proteins with orthologs in another given species, and the functions of
complexes detected using the entire network or sub-networks generated by random sampling
of proteins. We applied the proposed methodology to a Yeast PPI network using orthology
information from a number of different organisms. The results indicated that the proposed
method is capable to isolate functional categories that can be clearly attributed to the presence
of an evolutionary (orthology) signal and quantify their distribution at a fine-grained
protein level
Biclustering on expression data: A review
Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.Ministerio de Economía y Competitividad TIN2011-2895
Evolutionary Biclustering based on Expression Patterns
The majority of the biclustering approaches for
microarray data analysis use the Mean Squared Residue (MSR)
as the main evaluation measure for guiding the heuristic.
MSR has been proven to be inefficient to recognize several
kind of interesting patterns for biclusters. Transposed Virtual
Error (VEt ) has recently been discovered to overcome MSR
drawbacks, being able to recognize shifting and/or scaling
patterns. In this work we propose a parallel evolutionary
biclustering algorithm which uses VEt as the main part of
the fitness function, which has been designed using the volume
and overlapping as other objectives to optimize. The resulting
algorithm has been tested on both synthetic and benchmark
real data producing satisfactory results. These results has been
compared to those of the most popular biclustering algorithm
developed by Cheng and Church and based in the use of MSR.Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0
Measuring the Quality of Shifting and Scaling Patterns in Biclusters
The most widespread biclustering algorithms use the Mean Squared Residue (MSR) as measure for assessing the quality of biclusters. MSR can identify correctly shifting patterns, but fails at discovering biclusters presenting scaling patterns. Virtual Error (VE) is a measure which improves the performance of MSR in this sense, since it is effective at recognizing biclusters containing shifting patters or scaling patterns as quality biclusters. However, VE presents some drawbacks when the biclusters present both kind of patterns simultaneously. In this paper, we propose a improvement of VE that can be integrated in any heuristic to discover biclusters with shifting and scaling patterns simultaneously.Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0
Shifting Patterns Discovery in Microarrays with Evolutionary Algorithms
In recent years, the interest in extracting useful knowledge from gene expression data has experimented an enormous increase with the development of microarray technique. Biclustering is a recent technique that aims at extracting a subset of genes that show a similar behaviour for a subset conditions. It is important, therefore, to measure the quality of a bicluster, and a way to do that would be checking if each data submatrix follows a specific trend, represented by a pattern. In this work, we present an evolutionary algorithm for finding significant shifting patterns which depict the general behaviour within each bicluster. The empirical results we have obtained confirm the quality of our proposal, obtaining very accurate solutions for the biclusters used.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004-00159Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004-06689C030
Configurable Pattern-based Evolutionary Biclustering of Gene Expression Data
BACKGROUND: Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties. RESULTS: Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns). CONCLUSIONS: We have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology
Improved biclustering on expression data through overlapping control
Purpose – The purpose of this paper is to present a novel control mechanism for avoiding overlapping
among biclusters in expression data.
Design/methodology/approach – Biclustering is a technique used in analysis of microarray data.
One of the most popular biclustering algorithms is introduced by Cheng and Church (2000) (Ch&Ch).
Even if this heuristic is successful at finding interesting biclusters, it presents several drawbacks. The
main shortcoming is that it introduces random values in the expression matrix to control the
overlapping. The overlapping control method presented in this paper is based on a matrix of weights,
that is used to estimate the overlapping of a bicluster with already found ones. In this way, the algorithm
is always working on real data and so the biclusters it discovers contain only original data.
Findings – The paper shows that the original algorithm wrongly estimates the quality of the
biclusters after some iterations, due to random values that it introduces. The empirical results show that
the proposed approach is effective in order to improve the heuristic. It is also important to highlight that
many interesting biclusters found by using our approach would have not been obtained using the
original algorithm.
Originality/value – The original algorithm proposed by Ch&Ch is one of the most successful
algorithms for discovering biclusters in microarray data. However, it presents some limitations, the
most relevant being the substitution phase adopted in order to avoid overlapping among biclusters.
The modified version of the algorithm proposed in this paper improves the original one, as proven in the
experimentation.Ministerio de Ciencia y Tecnología TIN2007-68084-C02- 0
An effective measure for assessing the quality of biclusters
Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in data. Such heuristics have one common characteristic: they are guided by a measure that determines the quality of biclusters. It follows that defining such a measure is probably the most important aspect. One of the popular quality measure is the mean squared residue (MSR). However, it has been proven that MSR fails at identifying some kind of patterns. This motivates us to introduce a novel measure, called virtual error (VE), that overcomes this limitation. Results obtained by using VE confirm that it can identify interesting patterns that could not be found by MSR