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This paper presents a novel priority based data mining algorithm using improved K-means clustering for detecting proteins sequence from dataset of frequent item set. The priorities are set depending on the number of hits (counts) from the dataset concurrently using the concept of multiprocessing. Which dynamically changing for a period of time series, a novel algorithm is used for classification and...
Memetic algorithm (MA) often perform better than other evolutionary algorithm due to their combining the local search with the process of global optimization. However, like any other evolutionary algorithm (EA), MA due to the problem of genetic drift often result in sub-optimal solutions. The problem is more aggravated when EAs are applied to search complex landscape of NP complete problem like protein...
In this paper, we propose an average-degree based cluster mining algorithm (ACM) for complexes detection in PPI networks. ACM method contains of three stages. Firstly, we make use of PPI network topology, i.e., average degree, to present a new quantitative function and then present a hierarchical algorithm to identify protein complexes. Finally, post-processing is applied to the predicted results...
Clustering protein-protein interaction network aims to find functional modules and protein complexes. There are many computational graph clustering methods that are used in this field, but few of them are intelligent computational methods. In this paper, we present a novel improved immune genetic algorithm to find dense subgraphs based on efficient vaccination method, variable-length antibody schema...
Recent work using supervised learning for protein structure prediction has achieved state-of-the-art classification performance. However, such methods are based only on labeled data, while in practice the labeled data is so few and expensive to obtain and unlabeled data is far more plentiful. An effective way to enhance the performance of the learned hypothesis by using the labeled and unlabeled data...
With the recent high-throughput methods, large datasets of experimentally detected pairwise protein-protein interactions are generated. However, these data suffer from noise, reducing the quality of the information they bring (identification of protein complexes). This paper introduces a novel methodology for detecting protein complexes in a protein-protein interaction graph. Our method initially...
Interactomics is the study of the Interactome, i.e. the whole set of macromolecular interactions within a cell. Proteins interact among them and different interactions are represented as graphs named Protein to Protein Interaction (PPI) networks. The interest in analyzing PPI networks is related to the possibility of predicting PPI properties on the basis of global properties of the graph (e.g. verify...
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