This paper proposed a new multi-view separation algorithm and a new composite-three-view representation to deal with the practical engineering drawings using existing algorithms which can only handle three-view drawings. The proposed algorithm can separate the views in an engineering drawing automatically and group them into three composite views to meet the input requirements of existing algorithms, which extends the scope of engineering drawings can be handled.

Background 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. Results 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. Conclusions The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.

BACKGROUND: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.RESULTS: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.CONCLUSIONS:The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.

Multi-objective optimization (MOP) a fast growing area of research. Bioinformatics data sets come mostly from DNA microarray experiments. The analysis of microarray data sets can provide valuable information on the biological relevance of genes and correlations among them. Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. A single bicluster represents a given subset of genes in a given subset of conditions. For solving multiple objectives optimization, ant colony optimization algorithms have been shown to be very effective for MOP. This paper proposes online Multiple Objective Ant Colony Optimization biclustering algorithm to solve patterns mining problem of microarray dataset. During optimization, the size of ant population is dynamically changed to quicken the convergence of the algorithm. Experimental analysis on two real dataset shows that the proposed algorithm achieves good performance in the diversity of solution and the time complexity of the algorithm.

Biclustering of DNA microarray data that can mine significant patterns to help in understanding gene regulation and interactions. This is a classical multi-objective optimization problem (MOP). Recently, many researchers have developed stochastic search methods that mimic the efficient behavior of species such as ants, bees, birds and frogs, as a means to seek faster and more robust solutions to complex optimization problems. The particle swarm optimization(PSO) is a heuristics-based optimization approach simulating the movements of a bird flock finding food. The shuffled frog leaping algorithm (SFLA) 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. This paper introduces SFL algorithm to solve biclustering of microarray data, and proposes a novel multi-objective shuffled frog leaping biclustering(MOSFLB) algorithm to mine coherent patterns from microarray data. Experimental results on two real datasets show that our approach can effectively find significant biclusters of high quality.

This paper formulates the protein function prediction into a typical LPU.Aiming at imbalance or over-fitting from LPU with few positive examples,it proposes a method creating synthetic examples to enlarge the set of positive examples based on the nearest neighbor and convex combination,and meanwhile modifies the procedure learning optimal classifier for the classic LPU algorithm by using one-class SVM(support vector machine) to identify the most probable negative examples,running iteratively SVM to move the classification hyperplane to a suitable place and obtaining representative negative examples through cross validation.For the yeast genomic data,the experiments show that our algorithm outperforms several classic prediction methods,particularly,for function classes with few positive examples.

Most of optimization problems have more than one objective function. As a heuristic search technique, particle swarm optimization (PSO) simulates the movements of a flock of birds which aim to find food. The success of PSO has motivated researchers to extend the use of population-based technique to multi-objective optimization. Rapid development of the DNA microarray technology make it very possible to study the transcriptional response of a complete genome to different experimental conditions. Biclustering technique has successfully used to analysis those gene expression data. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective modeling is suitable for solving biclustering problem. Based on dynamic population, this paper proposes a novel dynamic multi-objective particle swarm optimization biclustering (DMOPSOB) algorithm to mine coherent patterns from microarray data. Experimental results on real datasets show that our approach can effectively find significant biclusters of high quality.

The miscellaneous and abstract concepts of discrete mathematics impedes seriously proposition learning,logical thought training and the forming of whole theory system,as a result,they has a bad impact on improving teaching quality of this course.On the basis of APOS theory proposed by Dubinsky,a famous mathematic educator,this paper proposed corresponding learning strategy for the concepts of discrete mathematics.Teaching practice show that concept teaching strategy is very effective for improving study interesting,training thought ability and improving teaching quality of discrete mathematics.

The paper proposes a novel hierarchical classification approach with dynamic-threshold SVM ensemble. At training phrase, hierarchical structure is explored to select suit positive and negative examples as training set in order to obtain better SVM classifiers. When predicting an unseen example, it is classified for all the label classes in a top-down way in hierarchical structure. Particulary, two strategies are proposed to determine dynamic prediction threshold for different label class, with hierarchical structure being utilized again. In four genomic data sets, experiments show that the selection policies of training set outperform existing two ones and two strategies of dynamic prediction threshold achieve better performance than the fixed thresholds.

Many bioinformatics data sets come from DNA microarray experiments. Biclustering of gene expression data can identify genes with similar behaviour with respect to different conditions. Ant Colony Optimisation (ACO) algorithms have been shown to be effective problem solving strategies for a wide range of problem domains. Multiple Objective Ant Colony Optimisation (MOACO) mainly focuses on solving the multiple objective combinatorial optimisation problems. This paper incorporates crowding update technology into MOACOB and proposes crowding MOACO biclustering algorithm to mine biclusters from gene expression data. Experimental results are shown for biclustering algorithm on two real gene expression data.

BACKGROUND:A large amount of functional genomic data have provided enough knowledge in predicting gene function computationally, which uses known functional annotations and relationship between unknown genes and known ones to map unknown genes to GO functional terms. The prediction procedure is usually formulated as binary classification problem. Training binary classifier needs both positive examples and negative ones that have almost the same size. However, from various annotation database, we can only obtain few positive genes annotation for most of functional terms, that is, there are only few positive examples for training classifier, which makes predicting directly gene function infeasible.RESULTS:We propose a novel approach SPE_RNE to train classifier for each functional term. Firstly, positive examples set is enlarged by creating synthetic positive examples. Secondly, representative negative examples are selected by training SVM (support vector machine) iteratively to move classification hyperplane to a appropriate place. Lastly, an optimal SVM classifier are trained by using grid search technique. On combined kernel of Yeast protein sequence, microarray expression, protein-protein interaction and GO functional annotation data, we compare SPE_RNE with other three typical methods in three classical performance measures recall R, precise P and their combination F: twoclass considers all unlabeled genes as negative examples, twoclassbal selects randomly same number negative examples from unlabeled gene, PSoL selects a negative examples set that are far from positive examples and far from each other.CONCLUSIONS:In test data and unknown genes data, we compute average and variant of measure F. The experiments show that our approach has better generalized performance and practical prediction capacity. In addition, our method can also be used for other organisms such as human.

This paper presents a new approach to reconstruct curved solids composed of elementary volumes intersecting with one another from three-view engineering drawings. Intersection curves arising from two intersecting curved surfaces are mostly higher order spatial curves, which cannot be described exactly by 2D orthographic projections and normally represented as smooth curves passing through several key points or even simplified as arcs or lines. Approximated sketches of higher order intersection curves in 2D views result in the invalidation of existing methods that need the exact projection information as input. Based on some heuristic hints, our method is able to recover the complete and correct half-profiles of the intersecting elementary volumes using the least traces left by them, which ensure the correctness of solution solids constructed finally. Several examples are provided to show the validation of the described method.

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.

High throughput technologies yield large-scale datasets on genomic variation in diverse populations, allowing the study of these variations and their association with disease and their complex traits. Systematic functional characterization of genes identified in the genome sequencing projects is urgently needed in the post-genomic era. Biclustering, which searches for subsets of individuals that are coherent in their behavior across a subset of the features, is a very useful data mining technique in microarray data analysis and has presented its advantages in many applications. This paper proposes a novel multi-objective immune biclustering (MOIB) algorithm, based on the immune response principle of the immune system, to mine biclusters from microarray data.In the algorithm, we extends ε-dominance and performs the mechanism of crowding computation to obtain many Pareto optimal solutions distributed onto the Pareto front. Experimental results on real datasets show that our approach can effectively find more significant biclusters than other biclustering algorithms.

Predicting gene function is usually formulated as binary classification problem. However, we only know which gene has some function while we are not sure that it doesn't belong to a function class, which means that only positive examples are given. Therefore, selecting a good training example set becomes a key step. In this paper, we cluster the genes on integrated weighted graph by generalizing the cluster coefficient of unweighted graph to weighted one, and identify the reliable negative samples based on distance between a gene and centroid of positive clusters. Then, the tri-training algorithm is used to learn three classifiers from labeled and unlabeled examples to predict the gene function by combining three prediction result. The experiment results show that our approach outperforms several classic prediction methods.

Latest microarray technique can measure the expression levels of thousands of genes under a set of conditions, and generates some large-scale microarray datasets. Biclustering can perform clustering of rows and columns of those dataset simultaneously, allowing the mining of additional information from microarray datasets which is important in bioinformatics research and biomedical applications. Since the biclustering problem is combinatorial, and multi-objective ant optimization systems present several advantages during dealing with this kind of problem. This paper proposes a novel multi-objective ant colony optimization biclustering algorithm to mine biclusters from microarray dataset. Experimental results on real dataset show that our approach can find significant biclusters of high quality.

Biclustering methods allow us to identify genes withsimilar behavior with respect to different conditions. AntColony Optimization (ACO) algorithms have been shownto be effective problem solving strategies for MultipleObjective Optimization (MOO). Multiple Objective Antcolony optimization (MOACO) mainly focuses on solvingthe multiple objective combinatorial optimizationproblems. This paper incorporates crowding updatetechnology into MOACOB and proposes a novel crowdingbased MOACO biclustering algorithm to mine biclustersfrom microarray dataset. Experimental results are shownfor biclustering algorithm on two real gene expressiondataset.

The clustering technique is widely used in microarray data analysis,and mining three-dimensional(3D)clusters in gene-sample-time(simply GST)microarray data is emerging as a hot research topic in this area.During the mining of 3D clusters,several objectives have to be optimized simultaneously,and often these objectives are in conflict with each other.Moreover,with great exploration power,evolutionary computation is made as an effective search approach in the search space of huge dimensionality.Based on MOEA(Multi-Objective Evolutionary Algorithm),this paper proposes a new 3D cluster algorithm,MOE-TC(Multi-Objective Evolutionary TriClustering),to mine 3D clusters in 3D microarray data.Experimental results in real microarray data confirm the validity of the proposed technique.

Using Multiple Kernels Learning(MKL) to integrate heterogeneous data sources to train Support Vector Machine(SVM) classifier is becoming popular. For the protein function prediction problem, all the function categories form a directed acyclic graph(DAG), that is, Gene Ontology(GO). Given a protein to be predicted, after applying a trained SVM to output probabilistic prediction at each function category node, we use a cost-based model to consistently adjust function assignment on GO, which is called PredConsist/MKL. Experiments show PredConsist/MKL has higher ROC score than unadjusted method SDP/SVM, and better prediction performance. This adjustment is necessary.

With the advent of the DNA microarray technology,it is now possible to study the transcriptional response of a complete genome to different experimental conditions. Biclustering is a very useful data mining technique for analysis of those gene expression data.During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective modeling is suitable for solving biclustering problem. This paper proposes a novel multi-objective particle swarm optimization biclustering (MOPSOB) algorithm to mine coherent patterns from microarray data. Experimental results on real datasets show that our approach can effectively find significant biclusters of high quality.