Béatrice Duval

A hybrid GA/SVM approach for gene selection and classification of microarray data

We propose a Genetic Algorithm (GA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to a fuzzy logic based pre-filtering technique. The GA is used to evolve gene subsets whose fitness is evaluated by a SVM classifier. Using archive records of ”good” gene subsets, a frequency based technique is introduced to identify the most informative genes. Our approach is assessed on two well-known cancer datasets and shows competitive results with six existing methods.

NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference

MOTIVATION Reconstruction of gene regulatory networks (GRNs) is of utmost interest to biologists and is vital for understanding the complex regulatory mechanisms within the cell. Despite various methods developed for reconstruction of GRNs from gene expression profiles, they are notorious for high false positive rate owing to the noise inherited in the data, especially for the dataset with a large number of genes but a small number of samples. RESULTS In this work, we present a novel method, namely NARROMI, to improve the accuracy of GRN inference by combining ordinary differential equation-based recursive optimization (RO) and information theory-based mutual information (MI). In the proposed algorithm, the noisy regulations with low pairwise correlations are first removed by using MI, and the redundant regulations from indirect regulators are further excluded by RO to improve the accuracy of inferred GRNs. In particular, the RO step can help to determine regulatory directions without prior knowledge of regulators. The results on benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge and experimentally determined GRN of Escherichia coli show that NARROMI significantly outperforms other popular methods in terms of false positive rates and accuracy. AVAILABILITY All the source data and code are available at: http://csb.shu.edu.cn/narromi.htm.

Advances in metaheuristics for gene selection and classification of microarray data

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy for classification. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. In this article, we summarize some recent developments of using metaheuristic-based methods within an embedded approach for gene selection. In particular, we put forward the importance and usefulness of integrating problem-specific knowledge into the search operators of such a method. To illustrate the point, we explain how ranking coefficients of a linear classifier such as support vector machine (SVM) can be profitably used to reinforce the search efficiency of Local Search and Evolutionary Search metaheuristic algorithms for gene selection and classification.

Identifying cancer-related microRNAs based on gene expression data

MOTIVATION MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in post-transcriptional regulations as well as other important biological processes. Recently, accumulating evidences indicate that miRNAs are extensively involved in cancer. However, it is a big challenge to identify which miRNAs are related to which cancer considering the complex processes involved in tumors, where one miRNA may target hundreds or even thousands of genes and one gene may regulate multiple miRNAs. Despite integrative analysis of matched gene and miRNA expression data can help identify cancer-associated miRNAs, such kind of data is not commonly available. On the other hand, there are huge amount of gene expression data that are publicly accessible. It will significantly improve the efficiency of characterizing miRNAs function in cancer if we can identify cancer miRNAs directly from gene expression data. RESULTS We present a novel computational framework to identify the cancer-related miRNAs based solely on gene expression profiles without requiring either miRNA expression data or the matched gene and miRNA expression data. The results on multiple cancer datasets show that our proposed method can effectively identify cancer-related miRNAs with higher precision compared with other popular approaches. Furthermore, some of our novel predictions are validated by both differentially expressed miRNAs and evidences from literature, implying the predictive power of our proposed method. In addition, we construct a cancer-miRNA-pathway network, which can help explain how miRNAs are involved in cancer. AVAILABILITY AND IMPLEMENTATION The R code and data files for the proposed method are available at http://comp-sysbio.org/miR_Path/ CONTACT liukeq@gmail.com SUPPLEMENTARY INFORMATION supplementary data are available at Bioinformatics online.

A hybrid LDA and genetic algorithm for gene selection and classification of microarray data

In supervised classification of Microarray data, gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy. This paper introduces a new embedded approach to this difficult task where a genetic algorithm (GA) is combined with Fishers linear discriminant analysis (LDA). This LDA-based GA algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDAs discriminant coefficients in its dedicated crossover and mutation operators. Computational experiments on seven public datasets show that under an unbiased experimental protocol, the proposed algorithm is able to reach high prediction accuracies with a small number of selected genes.

A memetic algorithm for gene selection and molecular classification of cancer

Choosing a small subset of genes that enables a good classification of diseases on the basis of microarray data is a difficult optimization problem. This paper presents a memetic algorithm, called MAGS, to deal with gene selection for supervised classification of microarray data. MAGS is based on an embedded approach for attribute selection where a classifier tightly interacts with the selection process. The strength of MAGS relies on the synergy created by combining a problem specific crossover operator and a dedicated local search procedure, both being guided by relevant information from a SVM classifier. Computational experiments on 8 well-known microarray datasets show that our memetic algorithm is very competitive compared with some recently published studies.

Fuzzy Logic for Elimination of Redundant Information of Microarray Data

Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, we use fuzzy inference rules to transform the gene expression levels of a given dataset into fuzzy values. Then we apply a similarity relation to these fuzzy values to define fuzzy equivalence groups, each group containing strongly similar genes. Dimension reduction is achieved by considering for each group of similar genes a single representative based on mutual information. To assess the usefulness of this approach, extensive experimentations were carried out on three well-known public datasets with a combined classification model using three statistic filters and three classifiers.

On the Discovery of Exception Rules: A Survey

In this paper, we present a survey of the different approaches developed for mining exception rules. Exception rules are interesting in the context of quality measures since such rules are intrinsically satisfied by few individuals in the database and many criteria relying on the number of occurrences, such as for instance the support measure, are no longer relevant. Therefore traditional measures must be coupled with other criteria. In that context, some works have proposed to use the experts knowledge: she/he can provide the system either with constraints on the syntactic form of the rules, thus reducing the search space, or with commonsense rules that have to be refined by the data mining process. Works that rely on either of these approaches, with their particular quality evaluation are presented in this survey. Moreover, this presentation also gives ideas on how numeric criteria can be intertwined with user-centered approaches.

Opposition-Based Memetic Search for the Maximum Diversity Problem

As a usual model for a variety of practical applications, the maximum diversity problem (MDP) is computational challenging. In this paper, we present an opposition-based memetic algorithm (OBMA) for solving MDP, which integrates the concept of opposition-based learning (OBL) into the well-known memetic search framework. OBMA explores both candidate solutions and their opposite solutions during its initialization and evolution processes. Combined with a powerful local optimization procedure and a rank-based quality-and-distance pool updating strategy, OBMA establishes a suitable balance between exploration and exploitation of its search process. Computational results on 80 popular MDP benchmark instances show that the proposed algorithm matches the best-known solutions for most of instances, and finds improved best solutions (new lower bounds) for 22 instances. We provide experimental evidences to highlight the beneficial effect of OBL for solving MDP.

Gene Selection for Microarray Data by a LDA-Based Genetic Algorithm

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy. This paper introduces a new wrapper approach to this difficult task where a Genetic Algorithm (GA) is combined with Fishers Linear Discriminant Analysis (LDA). This LDA-based GA algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDAs discriminant coefficients in its dedicated crossover and mutation operators. The proposed algorithm is assessed on a set of seven well-known datasets from the literature and compared with 16 state-of-art algorithms. The results show that our LDA-based GA obtains globally high classification accuracies (81%-100%) with a very small number of genes (2-19).