145 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 AlmerÃaLos 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 artÃculo 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
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 TecnologÃa TIN2007-68084-C02-0
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 EconomÃa y Competitividad TIN2011-2895
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 TecnologÃa 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 TecnologÃa (CICYT) TIN2004-00159Comisión Interministerial de Ciencia y TecnologÃa (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
Evolutionary Search of Biclusters by Minimal Intrafluctuation
Biclustering techniques aim at extracting significant
subsets of genes and conditions from microarray gene
expression data. This kind of algorithms is mainly based on two
key aspects: the way in which they deal with gene similarity
across the experimental conditions, that determines the quality
of biclusters; and the heuristic or search strategy used for
exploring the search space. A measure that is often adopted
for establishing the quality of biclusters is the mean squared
residue. This measure has been successfully used in many
approaches. However, it has been recently proven that the
mean squared residue fails to recognize some kind of biclusters
as quality biclusters, mainly due to the difficulty of detecting
scaling patterns in data. In this work, we propose a novel
measure for trying to overcome this drawback. This measure
is based on the area between two curves. Such curves are
built from the maximum and minimum standardized expression
values exhibited for each experimental condition. In order
to test the proposed measure, we have incorporated it into
a multiobjective evolutionary algorithm. Experimental results
confirm the effectiveness of our approach. The combination of
the measure we propose with the mean squared residue yields
results that would not have been obtained if only the mean
squared residue had been used.Comisión Interministerial de Ciencia y TecnologÃa (CICYT) TIN2004-0015
Knowledge-Based Fast Evaluation for Evolutionary Learning
The increasing amount of information available is encouraging
the search for efficient techniques to improve the data mining
methods, especially those which consume great computational resources,
such as evolutionary computation.Efficacy and efficiency are two critical
aspects for knowledge-based techniques.The incorporation of knowledge
into evolutionary algorithms (EAs) should provide either better solutions
(efficacy) or the equivalent solutions in shorter time (efficiency), regarding
the same evolutionary algorithm without incorporating such knowledge.
In this paper, we categorize and summarize some of the incorporation of
knowledge techniques for evolutionary algorithms and present a novel
data structure, called efficient evaluation structure (EES), which helps the
evolutionary algorithm to provide decision rules using less computational
resources.The EES-based EA is tested and compared to another EA
system and the experimental results show the quality of our approach,
reducing the computational cost about 50%, maintaining the global
accuracy of the final set of decision rules.CICYT TIN2004-0015
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