Data Mining for Systems Biology: Methods and Protocols by Hiroshi Mamitsuka, Charles DeLisi, Minoru Kanehisa

By Hiroshi Mamitsuka, Charles DeLisi, Minoru Kanehisa

The post-genomic revolution is witnessing the iteration of petabytes of information each year, with deep implications ranging throughout evolutionary thought, developmental biology, agriculture, and disorder tactics. info Mining for structures Biology: equipment and Protocols, surveys and demonstrates the technological know-how and expertise of changing an unheard of facts deluge to new wisdom and organic perception. the amount is geared up round overlapping subject matters, community inference and practical inference. Written within the hugely profitable equipment in Molecular Biology™ sequence structure, chapters comprise introductions to their respective subject matters, lists of the mandatory fabrics and reagents, step by step, easily reproducible protocols, and key pointers on troubleshooting and keeping off recognized pitfalls.   Authoritative and functional, info Mining for platforms Biology: tools and Protocols additionally seeks to assist researchers within the extra improvement of databases, mining and visualization platforms which are critical to the paradigm changing discoveries being made with expanding frequency.

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Extra resources for Data Mining for Systems Biology: Methods and Protocols (Methods in Molecular Biology)

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Network Evolution-Based Alignment In this section, we present a new alignment method, called PME, that is based on a probabilistic model of evolution. PME aims to reconstruct the most probable ancestral network that gave rise to the observed extant networks. Such a network induces a many-tomany alignment in the descending networks by associating groups of proteins in the two input networks with the corresponding ancestral proteins. The method is based on a probabilistic model of the evolutionary dynamics of a network, that supports four kinds of evolutionary events: link attachment, link detachment, gene duplication and gene loss (18).

To avoid an arbitrary choice among identically scored solutions, we choose the solution that agrees best with the sequencesimilarity information. To this end, we add a small penalty to each ancestral-descendant connection whose value is 10À8 Á logðS þ 1Þ, where S is the bit score of the two proteins. Although PME naturally produces a many-to-many correspondence between orthologous proteins, we focus here on its reduction to a one-to-one mapping to facilitate its comparison to other methods. To this end, we rank all pairs of inter-species proteins that are predicted to descend from the same common ancestor.

References 1. Fields S, Song O (1989) A novel genetic system to detect Protein–protein interactions. Nature 340(6230):245–246 2. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:198–207 3. Kelley BP, Yuan B, Lewitter F, Sharan R, Stockwell BR, Ideker T (2004) PathBLAST: a tool for alignment of protein interaction networks. Nucl Acids Res 32(Suppl 2): W83–W88 4. Sharan R, Suthram S, Kelley R, Kuhn T, McCuine S, Uetz P, Sittler T, Karp R, Ideker T (2005) Conserved patterns of protein interaction in multiple species.

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