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...is of little use for high dimensional GE analysis and, typically, heuristics are used. Simulated Annealing is well-known and has been applied [40] using TWCV to minimise the fitness function, E, and, =-=[41]-=-,by minimising H-Score (Eq. 1). At each stage of the process, gene vectors are randomly chosen and moved to a new random cluster. E is evaluated for each move and the new assignment 13is accepted if ...

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... problem as defined either by Cheng and Church (8) or by Aguilar-Ruiz (15) or variations, including the work by Kung et al. (16), Li et al. (17), Reiss et al. (11), Pedro et al. (18) and Bryan et al. =-=(9,10,19)-=-, to name a few, which has led to a number of publicly available computer servers for biclustering analysis of microarray data. Among the published biclustering servers, some have employed combinatori...

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...e these difficulties, the clustering and classification methods from the areas of statistical machine learning have been applied to cancer identification using molecular gene expression data [2]–[4], =-=[8]-=-, [9]. In this paper, we are interested in unsupervised clustering-based cancer class discovery, instead of supervised classification. Clustering does not require knowing apriorithe classes of trainin...

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...ction for all possible ways of cluster membership reassignment. This, however, is not computationally feasible. To overcome this difficulty, we used simulated annealing to minimize the loss function (=-=Bryan, et al., 2006-=-; Chakraborty, 2005). Model setup Let p C ={ x1, x2,..., xn} ⊂ R be a set of n data points in a pdimensional space (e.g. expression of n genes measured in p conditions). The purpose of our clustering ...

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...search approach includes greedy algorithms [6, 9, 10, 29], divideand-conquer algorithms [17, 26] and enumeration algorithms [4, 20]. The metaheuristic approach includes neighbourhood-based algorithms =-=[8]-=-, GRASP [12, 13]2 Ayadi, Elloumi, Hao and evolutionary algorithms [15, 16, 23]. A recent review of various biclustering algorithms for biological data analysis is provided in [3]. In this paper, we p...

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... been widely used in many proposals. These proposals are based on a diverse range of heuristics: greedy search [24], genetic algorithms (both single and multi-objective) [17, 25], simulated annealing =-=[26]-=-, and fuzzy biclustering, among others. More recently, MSR has also been incorporated as cost function in multiobjective heuristics based on Particle Swarm Optimization [27], Artificial Immune Systems...

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...upon greedy search strategies such as Expectation Maximization, by being able to escape local minima and therefore being able to more completely assign genes and conditions to clusters as appropriate =-=[24]-=-. Other stochastic or greedy search strategies may be applicable to solving this model, for example if speed is deemed to be a more important consideration than completeness of the solution. Additiona...