Download PDF by See-Kiong Ng, Xiao-Li Li: Biological Data Mining in Protein Interaction Networks

By See-Kiong Ng, Xiao-Li Li

ISBN-10: 1605663980

ISBN-13: 9781605663982

Equipment for detecting protein-protein interactions (PPIs) have given researchers an international photograph of protein interactions on a genomic scale.

organic information Mining in Protein interplay Networks explains bioinformatic tools for predicting PPIs, in addition to info mining how you can mine or research quite a few protein interplay networks. A defining physique of analysis in the box, this e-book discovers underlying interplay mechanisms by means of learning intra-molecular beneficial properties that shape the typical denominator of varied PPIs.

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The group that maintains BIND has also developed a graphical analysis tool that provides users an understanding of functional domains in protein interactions. They have also developed a clustering tool that allows users to divide the protein interaction network into specific regions of interest. BIND assumes that interactions can occur between two biological ‘objects’, which could be proteins, RNA or DNA sequences, genes, molecular complexes, small molecules, or photons (light). , 2004) is a database containing 18,343 interactions between 4,923 proteins validated from 23,366 experiments of the Saccharomyces cerevisiae organism.

Nucleic Acids Research, 34, D247-D251. , & Akutsu, T. (2003). Inferring strengths of protein-protein interactions from experimental data using linear programming. Bioinformatics, 19, ii58-ii65. , & Akutsu, T. (2004). A simple method for inferring strengths of protein-protein interactions. Genome Informatics, 15-1, 56-68. , Krapivsky, P. , & Yuryev, A. (2005). Duplication-divergence model of protein interaction network. Physical Review E, 71, 061911. , & Sakaki, Y. (2001). A comprehensive two-hybrid analysis to explore the yeast protein interactome.

The single feature in this category was extracted by taking the union of lethality indicators from Tong et al. , 2002). Gene neighborhood/Gene Fusion/Gene Co-occurrence: The single feature in this category is the disjunction of indicators from the three datasets described by von Mering et al. (2002). , 2004). Homology-based PPI: Sequence similarity information is used to identify homology pairs. These pairs are then “BLASTed” against NCBI’s non-redundant protein database and the count of their interactions extracted, resulting in four features in this category.

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Biological Data Mining in Protein Interaction Networks by See-Kiong Ng, Xiao-Li Li

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