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

By See-Kiong Ng, Xiao-Li Li

ISBN-10: 1605663980

ISBN-13: 9781605663982

Tools 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 information mining how to mine or study quite a few protein interplay networks. A defining physique of study in the box, this ebook discovers underlying interplay mechanisms by way of learning intra-molecular positive factors that shape the typical denominator of varied PPIs.

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Extra resources for Biological Data Mining in Protein Interaction Networks

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Among these, the model proposed by Barabási and Albert (1999) is well-known, which is described as follows. 1. 2. 3. Create a complete graph with N0 nodes, where N0 is a constant greater than 1. Create a new node v and connect it to m nodes in the current graph, where each node is selected with the probability proportional to its degree, and m is a constant greater than 1. Repeat (2) until the number of nodes reaches the specified number. They showed that this procedure generates networks following the power-law of P(k ) ∝ k −3 , regardless of N0 and m.

In ASNM, contributions of these two protein pairs to (D1, D2) are equally treated. However, it is reasonable to give more weight to (P1, P2) than to (P1, P6). Chen et al. (2006) noticed this point and developed an alternative scoring scheme, which is called APM and is defined as below: APM ( Dm , Dn ) = ∑ ( i , j ):Dmn ∈Pij 1 − (1 −  N mn ij 1/| Pij | )   . , 2006). , strengths of known interactions). The scoring schemes are summarized in Fig. 4. 36 Domain-Based Prediction and Analysis of Protein-Protein Interactions Figure 4.

CONCLUDING REMARKS In this chapter, we have reviewed domain-based models both for prediction of protein-protein interactions and for explaining the scale-freeness of protein-protein interaction networks. Though the reviewed models are quite simple, it seems that these models capture important factors of the complex mechanisms of protein-protein interactions. Of course, as reviewed in the other chapters, there exist other important factors in protein-protein interactions. In particular, such factors as three-dimensional structures, sequence and structural motifs, physico-chemical properties of interface sites and evolutionary conserved regions should be taken into account.

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

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