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The detection of protein complexes from protein-protein interaction (PPI) networks is an important step toward understanding the functional organization within cells. A great number of graph clustering algorithms have been proposed to undertake this task. Since PPI data collected by high-throughput technologies is quite noisy, simply applying graph clustering algorithms on PPI data is generally not...
Proteomics is defined as the large-scale characterization of protein sets expressed in a cell or tissue. Lately, proteomics has been broadly using two-dimensional gel electrophoresis for its analysis. It consists of migration and separation of molecules, placed in a gel, according to the strength of an electric field. In order to see these proteins, it is necessary to use some kind of reagent of revelation,...
Clustering, or unsupervised classification, is an important problem in bioinformatics which serves to automatically group protein sequences into families. In this paper we explain the process of our approach. In the first part, we present extraction phase and features weighting subsequently features selecting. Then we explain our new distance equation and finally we describe the clustering method:...
Recently, liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technique for identifying differential abundance of peaks as biomarkers. Two major problems in the preprocessing of LC-MS data analysis are how to adjust and align multiple LC-MS datasets efficiently and correctly. Hence, an effective algorithm is needed to adjust the variation in retention time and align protein...
In recent years the effects of industrial growth have resulted in drastic changes in consuming behavior, standard of living, and lifestyles. In particular there has been a significant rise in the number of people who eat out regularly. This often means that they cannot control the characteristics of the food they take, such as its taste, calories per portion, and other nutritional details. As a result...
There has been a great deal of recent interest in identifying functional modules from protein interaction and gene expression data. One commonly used computational technique is simulated annealing, which while asymptotically correct frequently suffers from slow convergence. In this paper we outline and exploit the analogy between finding functional modules and finding Haplotype Blocks from genetic...
In data mining, there is inter-category imbalance of data which includes unnecessary data that hinder the formulation of an efficient model. This paper called FSFC+ introduces a new focused sampling based on adaptive fuzzy clustering. By applying FSFC+, the optimal number of clusters was used by adaptive method. It removes unuseful data that can be obstacles to the formulation of an efficient model...
Cluster analysis is widely applied to discover the function of previously unannotated genes. This paper presents a novel stratified beta-Gaussian mixture model, sBGMM, for clustering genes based on gene expression data, protein-DNA binding data and data that can provide information for constructing priors such as protein-protein interaction (PPI) data. An expectation maximization (EM) type of algorithm...
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