M. DABBA Ali

Cancer classification based on microarray data plays a very important role in cancer diagnosis and detection. Indeed, since microarray data contains a huge number of genes and a small number of samples, it is also nonlinear and noisy, which has led to the need to find a way to reduce the data dimensionality. In order to solve this problem, we need to find an effective way to help biologists and medical research scientists. This paper proposes a new bio-inspired algorithm for cancer classification in gene selection called Binary Grey Wolf Optimization Algorithm (BGWOA), which is based on hybridization between Minimum Redundancy-Maximum Relevance (MRMR) and a novel Binary Grey Wolf algorithm. The BGWOA is composed of two stages: The first stage consists of the MRMR pre-filter to obtain the set of relevant genes that reduces the dimensionality of the data sets. The second stage consists of a new Binary Grey Wolf algorithm based on direct similarity and centroid known in the geometric field to update the positions of grey wolves in order to exploit and explore the search spaces. As well, we used a fitness function that depends on the SVM with LOOCV classifier and the rate of unselected genes to evaluate the presented solutions. The primary goal of the last stage is to identify the best relevant subset of genes among those obtained in the first stage. This research used eight microarray datasets to evaluate and compare the proposed method with other existing algorithms. The experimental results produced in this research are able to provide a higher classification accuracy with fewer genes compared to many recently published algorithms. Specifically, the proposed method achieves 100% classification accuracy in five reference datasets with a number of genes ranging from 12 to 25. Therefore, this indicates that our research is promising and significant.

Abstract. Cancer classification is an important issue addressed in the Bioinformatics field. In this paper, we present a novel extension of the Moth Flame Optimization Algorithm combined with Mutual Information Maximization (MIM) to solve gene selection problem called Mutual Information Maximization-modified Moth Flame Optimization Algorithm (MIM-mMFOA). The MIM-mMFOA has two phases: the first one is used to solve the difficulty of high-dimensional data, which measures redundancy and relevance of the gene, in order to obtain the relevant gene set. The second phase is dedicated to finding a small gene subset that can be used to classify samples with high accuracy, using a Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV) classifier. In order to evaluate the performance of the proposed MIM-mMFOA, we test it on seven Microarray datasets. Experimental results show that MIM-mMFOA achieves a high classification accuracy in comparison to some known algorithms.

The knowledge of the protein structural class is one of the most important sources of information in many biological fields, such as function analysis, protein structure, drug design, and protein folding. However, the protein structural class prediction is still a challenge when dealing with low similarity sequences. Therefore, the accuracy of the top-performing prediction methods remains unsatisfying, especially for proteins from the + ß class. This paper proposes a novel approach for Protein Structural Class Prediction using a Convolutional Neural Network (PSCP-CNN). Our approach consists of two stages. The first is the preprocessing stage which allows the preparation of the data. The second stage is a CNN classifier that automatically extracts the needed features for the classification. To evaluate the performance of our approach, we performed the jackknife test on four low similarity benchmark datasets: 25PDB, 640, 1189, and FC699. The experimental results show that PSCP-CNN achieved high prediction accuracy, where the overall accuracy on datasets 25PDB, 640, 1189, and FC699 is 93.8%, 94.5%, 94.0%, and 98.0%, respectively. Furthermore, comparing the results obtained with existing methods shows that PSCP-CNN outperforms state-of-the-art techniques and confirms that using a convolutional neural network allows a better prediction of protein structural classes.

Cancer classification is one of the main applications of gene expression data (microarray data) and is essential for a comprehensive diagnosis of cancer treatment. Therefore, bio-inspired algorithms have developed several effective applications in the analysis of gene selection, which are one of the most effective applied in this domain. Harris Hawks optimization is a novel and recent algorithm that has an excellent balance between exploration and exploitation. This paper presents the first study on multi-objective binary Harris Hawks optimization (MOBHHO) for gene selection. We define gene selection as a problem, including two main conflicting objectives: minimizing the number of genes and maximizing the classification accuracy. MOBHHO uses two fitness functions to solve competing objectives. The first function based on SVM with LOOCV classifier and the second function also depends on KNN with K-fold classifier, as well as the percentage of gene selection found in both functions. Furthermore, MOBHHO tries to find the Pareto-optimal solutions, i.e. the best gene subset that contains a minimal number of selected genes and better classification accuracy. We have integrated several filter-based ranking methods with our proposal. In order to test the performance accuracy of the proposed MOBHHO algorithm, we compared our algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on eight benchmarks (binary-class and multi-class), MOBHHO able to provide a minimum number of genes to obtain the highest classification accuracy. The proposed method reaches above 98% classification accuracy in six benchmark datasets and a maximum accuracy of 100% is achieved.

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test QMFOA on thirteen microarray datasets (six binary-class and seven multi-class) to evaluate and compare the classification accuracy and the number of genes selected by the QMFOA against many recently published algorithms. Experimental results show that QMFOA provides greater classification accuracy and the ability to reduce the number of selected genes compared to the other algorithms.

In this paper, we present a new swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization algorithm (MFOA). The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QM- FOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is hybridization among MFOA, quantum computing, and support vector machine (SVM) with leave-one-out cross-validation (LOOCV), in order to solve the gene selection problem. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase.

In order to assess the performance of the proposed QMFOA, we test it on six Microarray datasets. Experimental results show that QMFOA provides great classification accuracy in comparison to some known algorithms.

Several techniques or methods may help in detecting diseases and cancer. Creating an effective method for extracting disease information is one of the major challenges in the classification of gene expression data as long as there is (in the presence) a massive amount of redundant data and noise. Bio-inspired algorithms are among the most effective when used for solving gene selection. Moth Flame Optimization Algorithm (MFOA) is computationally less expensive and can converge faster than other methods.

In this paper, we propose a new extension of the MFOA called the modified Moth Flame Algorithm (mMFA), the mMFA is combined with Mutual Information Maximization (MIM) to solve gene selection in microarray data classification. Our approach Called Mutual Information Maximization – modified Moth Flame Algorithm (MIM-mMFA), the MIM based pre-filtering technique is used to measure the relevance and the redundancy of the genes, and the mMFA is used to evolve gene subsets and evaluated by the fitness function, which uses a Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV) classifier and the number of selected genes. In order to test the performance of the proposed MIM-mMFA algorithm, we compared the MIM-mMFA algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on sixteen benchmark datasets either binary-class or multi-class, confirm that MIM-mMFA algorithm provides a greater classification accuracy.

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with single-objective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.

Dans les réseaux de capteurs sans fil (RCSF), un
problème très important est abordé dans la littérature est celui de
la couverture de frontières. Nous avons proposé un nouveau
protocole de couverture de frontières pour les RCSFs, nommé
BCP (Border Coverage Protocol) basé sur deux concepts, les
ensembles dominants de cardinalité minimale et le polygone de
voronoï. Ce protocole traite les trois points principaux : la
détection des nœuds frontières, la détection et la sélection des
nœuds internes et transfert, et la maintenance des nœuds
frontières. Nous avons présenté un modèle analytique pour ce
protocole, qui repose sur les chaînes de Markov. Le
comportement de chaque nœud du réseau est modélisé par une
chaîne de Markov.

Dans les réseaux de capteurs sans fil (RCSF), un problème très important est abordé dans la littérature est celui de la couverture de frontières. Nous avons proposé un nouveau protocole de couverture de frontières pour les RCSFs, nommé BCP (Border Coverage Protocol) basé sur deux concepts, les ensembles dominants de cardinalité minimale et le polygone de voronoï. Ce protocole traite les trois points principaux : la détection des nœuds frontières, la détection et la sélection des nœuds internes et transfert, et la maintenance des nœuds frontières. Nous avons présenté un modèle analytique pour ce protocole, qui repose sur les chaînes de Markov. Le comportement de chaque nœud du réseau est modélisé par une chaîne de Markov.

Border coverage is acknowledged as important problem addressed in literature of wireless sensor networks (WSNs). In this paper, we suggest a solution to solve this problem called Border Coverage Protocol (BCP) that preserves both border and area coverage. This protocol runs in three phases: the first one is used to detect the nodes close to the border and that will be considered as boundaries nodes. The second phase is dedicated to find the transfer and internal nodes. The last phase focused on replacing border nodes by the transfer nodes, in case of any failure. Simulation results show that BCP achieves a high coverage ratio in comparison to some known protocols.