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Biological Data Mining in Protein Interaction Networks - download pdf or read online

By See-Kiong Ng, Xiao-Li Li

ISBN-10: 1605663980

ISBN-13: 9781605663982

Tools for detecting protein-protein interactions (PPIs) have given researchers a world photograph of protein interactions on a genomic scale.

organic information Mining in Protein interplay Networks explains bioinformatic equipment 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 publication discovers underlying interplay mechanisms by way of learning intra-molecular beneficial properties that shape the typical denominator of varied PPIs.

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Additional info for Biological Data Mining in Protein Interaction Networks

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The database also provides information on the molecular structure and the functional network of the yeast genome. Introduction to Machine Learning Machine learning algorithms (Mitchell, 1997) offer some of the most cost-effective approaches to automated knowledge discovery and data mining (discovery of features, correlations, and other complex relationships and hypotheses that describe potentially interesting regularities) from large data sets. In particular, machine learning algorithms have proven to be very successful for many bioinformatics problems, including protein-protein interaction prediction.

The probability that such a pair is selected is proportional to x–γ · y–γ if nA = x and nB = y. Then, we will have x proteins with degree y, and y proteins with degree x. Thus, the expected number of proteins having degree y is approximated by Pr(nB = y ) ⋅ E[nA ] ∝ y − ∫ ∞ x =1 x − dx = 1 y− , −2 where E[Z] means the expected value of Z. We need to repeat this procedure for N times. We can also show that the degree distribution is approximately proportional to y–γ if N is not too large, and thus we can obtain the scale-free degree distribution of protein-protein interaction networks.

2001). Lethality and centrality in protein networks. Nature, 411, 41-42. Karev, G. , Wolf, Y. I, Rzhetsky, A. , Berezovskaya, F. , & Koonon, E. V. (2002). Birth and death of protein domains: A simple model of evolution explains power law behavior. BMC Evolutionary Biology, 2, 18. , & Zhao, H. (2007). Bayesian methods for predicting interacting protein pairs using domain information. Biometrics, 63, 824-833. Kim, P. , Lu, L. , & Gerstein, M. B. (2006). Relating three-dimensional structures to protein networks provides evolutionary insights.

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


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