In this paper, we present an algorithm to search and rank top-k approximately matched subtrees from a tree database, where the query is a collection of trees i.e. a forest. Even though existing algorithms can handle a single tree query, we argue that forest query would be significantly useful in some real life applications including biological domain. To address the issue we have proposed a method to find relevant subtrees and rank those given a tree database and a forest query. Tree edit distance is used to find and rank a set of subtrees with a pruning technique to improve the performance of the algorithm. We have tested our algorithm on different data sets and the efficiency of the searching and ranking process show promising results. Experimental results suggest that our algorithm improve run time at this stage and in future we would like to make it more useful for practical large data set.
Protein can be represented by amino acid interaction network. This network is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. This interaction network is the first step of proteins three-dimensional structure prediction. In this paper we present a multi-objective evolutionary algorithm for interaction prediction and ant colony probabilistic optimization algorithm is used to confirm the interaction.
Gene regulatory network is the network of genes interacting with each other performing as functional circuitry inside a cell. Many cellular processes are controlled by this network as they govern the expression levels of genes or gene product. High performance computational techniques are needed to analyze these data as it is heavily affected by noise. There are a number of algorithms available in the literature which use recurrent neural network for model building together with differential evolution, particle swarm optimization or genetic algorithm for searching the regulatory network. The problem with these methods is that they may trap in the local minimum. In this paper, we present an algorithm using recurrent neural network as model and an extended artificial bee colony algorithm for searching regulatory network that can avoid local minimum. A comprehensive analysis on both artificial and real data shows the effectiveness of the proposed approach. Furthermore we have also varied the network dimension and the noise level present in gene expression profiles. The reconstruction method has successfully predicted the underlying network topology while maintaining high accuracy. The proposed approach has also been applied to the real expression data of SOS DNA repair system in Escherichia coli and successfully predicted important regulations.
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