Jesús S. Aguilar-Ruiz

Incremental wrapper-based gene selection from microarray data for cancer classification

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.

Biclustering of expression data with evolutionary computation

Microarray techniques are leading to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In this work, we address the biclustering of gene expression data with evolutionary computation. Our approach is based on evolutionary algorithms, which have been proven to have excellent performance on complex problems, and searches for biclusters following a sequential covering strategy. The goal is to find biclusters of maximum size with mean squared residue lower than a given /spl delta/. In addition, we pay special attention to the fact of looking for high-quality biclusters with large variation, i.e., with a relatively high row variance, and with a low level of overlapping among biclusters. The quality of biclusters found by our evolutionary approach is discussed and the results are compared to those reported by Cheng and Church, and Yang et al. In general, our approach, named SEBI, shows an excellent performance at finding patterns in gene expression data.

Shifting and scaling patterns from gene expression data

MOTIVATION During the last years, the discovering of biclusters in data is becoming more and more popular. Biclustering aims at extracting a set of clusters, each of which might use a different subset of attributes. Therefore, it is clear that the usefulness of biclustering techniques is beyond the traditional clustering techniques, especially when datasets present high or very high dimensionality. Also, biclustering considers overlapping, which is an interesting aspect, algorithmically and from the point of view of the result interpretation. Since the Cheng and Churchs works, the mean squared residue has turned into one of the most popular measures to search for biclusters, which ideally should discover shifting and scaling patterns. RESULTS In this work, we identify both types of patterns (shifting and scaling) and demonstrate that the mean squared residue is very useful to search for shifting patterns, but it is not appropriate to find scaling patterns because even when we find a perfect scaling pattern the mean squared residue is not zero. In addition, we provide an interesting result: the mean squared residue is highly dependent on the variance of the scaling factor, which makes possible that any algorithm based on this measure might not find these patterns in data when the variance of gene values is high. The main contribution of this paper is to prove that the mean squared residue is not precise enough from the mathematical point of view in order to discover shifting and scaling patterns at the same time. CONTACT aguilar@lsi.us.es.

Evolutionary learning of hierarchical decision rules

This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must therefore be tried until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside of one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We tested our system on real data from the UCI repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.

An evolutionary approach to estimating software development projects

Abstract The use of dynamic models and simulation environments in connection with software projects paved the way for tools that allow us to simulate the behaviour of the projects. The main advantage of a Software Project Simulator (SPS) is the possibility of experimenting with different decisions to be taken at no cost. In this paper, we present a new approach based on the combination of an SPS and Evolutionary Computation. The purpose is to provide accurate decision rules in order to help the project manager to take decisions at any time in the development. The SPS generates a database from the software project, which is provided as input to the evolutionary algorithm for producing the set of management rules. These rules will help the project manager to keep the project within the cost, quality and duration targets. The set of alternatives within the decision-making framework is therefore reduced to a quality set of decisions.

Gene association analysis: a survey of frequent pattern mining from gene expression data

Establishing an association between variables is always of interest in genomic studies. Generation of DNA microarray gene expression data introduces a variety of data analysis issues not encountered in traditional molecular biology or medicine. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. We review the most relevant FPM strategies, as well as surrounding main issues when devising efficient and practical methods for gene association analysis (GAA). We observed that, so far, scalability achieved by efficient methods does not imply biological soundness of the discovered association patterns, and vice versa. Ideally, GAA should employ a balanced mining model taking into account best practices employed by methods reviewed in this survey. Integrative approaches, in which biological knowledge plays an important role within the mining process, are becoming more reliable.

Finding representative patterns with ordered projections

This paper presents a new approach to 2nding representative patterns for dataset editing. The algorithm patterns by ordered projections (POP), has some interesting characteristics: important reduction of the number of instances from the dataset; lower computational cost (� (mn log n)) with respect to other typical algorithms due to the absence of distance calculations; conservation of the decision boundaries, especially from the point of view of the application of axis-parallel classi2ers. POP works well in practice withbothcontinuous and discrete attributes. The performance of POP is analysed in two ways: percentage of reduction and classi2cation. POP has been compared to IB2, ENN and SHRINK concerning the percentage of reduction and the computational cost. In addition, we have analysed the accuracy of k-NN and C4.5 after applying the reduction techniques. An extensive empirical study using datasets with continuous and discrete attributes from the UCI repository shows that POP is a valuable preprocessing method for the later application of any axis-parallel learning algorithm. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Detecting Fault Modules Applying Feature Selection to Classifiers

At present, automated data collection tools allow us to collect large amounts of information, not without associated problems. This paper, we apply feature selection to several software engineering databases selecting attributes with the final aim that project managers can have a better global vision of the data they manage. In this paper, we make use of attribute selection techniques in different datasets publicly available (PROMISE repository), and different data mining algorithms for classification to defect faulty modules. The results show that in general, smaller datasets with less attributes maintain or improve the prediction capability with less attributes than the original datasets.

Biclustering on expression data

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.

A biclustering algorithm for extracting bit-patterns from binary datasets

MOTIVATION Binary datasets represent a compact and simple way to store data about the relationships between a group of objects and their possible properties. In the last few years, different biclustering algorithms have been specially developed to be applied to binary datasets. Several approaches based on matrix factorization, suffix trees or divide-and-conquer techniques have been proposed to extract useful biclusters from binary data, and these approaches provide information about the distribution of patterns and intrinsic correlations. RESULTS A novel approach to extracting biclusters from binary datasets, BiBit, is introduced here. The results obtained from different experiments with synthetic data reveal the excellent performance and the robustness of BiBit to density and size of input data. Also, BiBit is applied to a central nervous system embryonic tumor gene expression dataset to test the quality of the results. A novel gene expression preprocessing methodology, based on expression level layers, and the selective search performed by BiBit, based on a very fast bit-pattern processing technique, provide very satisfactory results in quality and computational cost. The power of biclustering in finding genes involved simultaneously in different cancer processes is also shown. Finally, a comparison with Bimax, one of the most cited binary biclustering algorithms, shows that BiBit is faster while providing essentially the same results. AVAILABILITY The source and binary codes, the datasets used in the experiments and the results can be found at: http://www.upo.es/eps/bigs/BiBit.html CONTACT dsrodbae@upo.es SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.