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ABSTRACT: Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets.
This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions.
The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.
BMC Genomics 01/2012; 13 Suppl 3:S6. · 4.07 Impact Factor
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2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Atlanta, GA, USA, November 12-15, 2011; 01/2011
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2011 IEEE International Conference on Granular Computing, GrC-2011, Kaohsiung, Taiwan, November 8-10, 2011; 01/2011
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ABSTRACT: Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem.
Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner.
The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.
BMC Genomics 01/2011; 12 Suppl 2:S11. · 4.07 Impact Factor
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Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II; 01/2010
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2010 IEEE International Conference on Granular Computing, GrC 2010, San Jose, California, USA, 14-16 August 2010; 01/2010
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I. J. Functional Informatics and Personalised Medicine. 01/2010; 3:58-72.
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ABSTRACT: During the last few years, Kernel methods have gained considerable attention for analyzing biological data for protein function
prediction. Based on biological processes annotation of Yeast and GO(gene ontology), we constructed a kernel matrix to predict
protein functions. We used measurement method about semantic similarity on GO and adaptive Hausdorff distance to successfully
obtain protein similarity matrix, and furthermore, transformed protein similarity matrix to a undirected graph. Then, We developed
a novel method that can learn optimal diffusion kernel from graph by maximizing kernel-target alignment. Experimental results
illustrate that the kernel matrix generated by our formula has larger AUC value than ordinary diffusion kernel and those proposed
before. Our method can even learn a common optimal kernel matrix for multiple predict tasks at one run. Furthermore, it can
also be directly used to learn from various biolobical networks.
05/2009: pages 694-703;
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ABSTRACT: High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.
In this work we present the CMOPSOB (Crowding distance based Multi-objective Particle Swarm Optimization Biclustering), a novel clustering approach for microarray datasets to cluster genes and conditions highly related in sub-portions of the microarray data. The objective of biclustering is to find sub-matrices, i.e. maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a subset of conditions. Since these objectives are mutually conflicting, they become suitable candidates for multi-objective modelling. Our approach CMOPSOB is based on a heuristic search technique, multi-objective particle swarm optimization, which simulates the movements of a flock of birds which aim to find food. In the meantime, the nearest neighbour search strategies based on crowding distance and -dominance can rapidly converge to the Pareto front and guarantee diversity of solutions. We compare the potential of this methodology with other biclustering algorithms by analyzing two common and public datasets of gene expression profiles. In all cases our method can find localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner.
The proposed CMOPSOB algorithm is successfully applied to biclustering of microarray dataset. It achieves a good diversity in the obtained Pareto front, and rapid convergence. Therefore, it is a useful tool to analyze large microarray datasets.
BMC Bioinformatics 02/2009; 10 Suppl 4:S9. · 2.75 Impact Factor
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The 2009 IEEE International Conference on Granular Computing, GrC 2009, Lushan Mountain, Nanchang, China, 17-19 August 2009; 01/2009
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2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, Washington, DC, USA, 1-4 November 2009, Proceedings; 01/2009
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The 2009 IEEE International Conference on Granular Computing, GrC 2009, Lushan Mountain, Nanchang, China, 17-19 August 2009; 01/2009
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Fifth International Conference on Natural Computation, ICNC 2009, Tianjian, China, 14-16 August 2009, 6 Volumes; 01/2009
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2008 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2008, 3-5 November 2008, Philadephia, Pennsylvania, USA; 01/2008
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The 2008 IEEE International Conference on Granular Computing, GrC 2008, Hangzhou, China, 26-28 August 2008; 01/2008
