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      Network-based prediction of protein function

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          Abstract

          Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

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          Most cited references 80

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Is Open Access

            Community structure in social and biological networks

            A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.
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              Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

              A high-capacity system was developed to monitor the expression of many genes in parallel. Microarrays prepared by high-speed robotic printing of complementary DNAs on glass were used for quantitative expression measurements of the corresponding genes. Because of the small format and high density of the arrays, hybridization volumes of 2 microliters could be used that enabled detection of rare transcripts in probe mixtures derived from 2 micrograms of total cellular messenger RNA. Differential expression measurements of 45 Arabidopsis genes were made by means of simultaneous, two-color fluorescence hybridization.
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                Author and article information

                Journal
                Mol Syst Biol
                Molecular Systems Biology
                1744-4292
                2007
                13 March 2007
                : 3
                : 88
                Affiliations
                [1 ]School of Computer Science, Tel Aviv University, Tel Aviv, Israel
                Author notes
                [a ]School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. Tel.: +972 3 6405383; Fax: +972 3 6405384; rshamir@ 123456tau.ac.il
                [*]

                These authors contributed equally to this work.

                Article
                msb4100129
                10.1038/msb4100129
                1847944
                17353930
                Copyright © 2007, EMBO and Nature Publishing Group
                Page count
                Pages: 1
                Categories
                Review Article

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