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

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

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

A novel approach for avoiding overlapping among biclusters in expression data

Biclustering is a technique used in analysis of microarray data. It aims at discovering subsets of genes that presents the same tendency under a subsest of experimental conditions. Various techniques have been introduced for discovering significant biclusters. One of the most popular heuristic was introduced by Cheng and Church [6]. In the same work, a measure, called mean squared residue, for estimating the quality of biclusters was proposed. Even if this heuristic is successful in finding interesting biclusters, it presents a number of drawbacks. In this paper we expose these drawbacks and propose some solutions in order to overcome them. Experiments show that the proposed solutions are effective in order to improve the heuristic.Ministerio de Ciencia y Tecnología TIN2007-68084-C02- 0

Virtual Error: A New Measure for Evolutionary Biclustering

Many heuristics used for finding biclusters in microarray data use the mean squared residue as a way of evaluating the quality of biclusters. This has led to the discovery of interesting biclusters. Recently it has been proven that the mean squared residue may fail to identify some interesting biclusters. This motivates us to introduce a new measure, called Virtual Error, for assessing the quality of biclusters in microarray data. In order to test the validity of the proposed measure, we include it within an evolutionary algorithm. Experimental results show that the use of this novel measure is effective for finding interesting biclusters, which could not have been discovered with the use of the mean squared residue

Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster

Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-2778Ministerio de Economía, Industria y Competitividad BFU2016-74975-PMinisterio de Ciencia e Innovación PID2019-103900GB-10