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Wiley Interdisciplinary Reviews Computational Molecular Science
Article . 2012 . Peer-reviewed
License: Wiley Online Library User Agreement
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Predictions of protein–RNA interactions

Authors: Cirillo D.; Agostini F.; Tartaglia G. G.;

Predictions of protein–RNA interactions

Abstract

AbstractRibonucleoprotein interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA–protein interactions provide information about the complexity of interaction networks, but require time and considerable efforts. Thus, there is need for reliable computational methods for predicting ribonucleoprotein interactions. In this review, we discuss a number of approaches that have been developed to predict the ability of proteins and RNA molecules to associate. © 2012 John Wiley & Sons, Ltd.This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

Country
Italy
Keywords

Ribonucleoprotein interactions; RNA-protein interactions; computational methods

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Top 10%
Green