High-Quality Binary Protein Interaction Map of the Yeast Interactome Network

Yu, Haiyuan; Braun, Pascal; Yıldırım, Muhammed A.; Lemmens, Irma; Venkatesan, Kavitha; Sahalie, Julie M.; Hirozane-Kishikawa, Tomoko; Gebreab, Fana; Li, Nan; Simonis, Nicolas; Hao, Tong; Rual, Jean François; Dricot, Amélie; Vázquez, Alexei; Murray, Ryan R.; Simon, Christophe; Tardivo, Leah; Tam, Stanley; Svrzikapa, Nenad; Fan, Changyu; Smet, A. De; Motyl, Adriana; Hudson, Michael E.; Park, Juyong; X, Xin; Cusick, Michael E.; Moore, Troy; Boone, Charlie; Snyder, M; Roth, Frederick P.; Barabási, Albert‐László; Tavernier, Jan; Hill, David E.; Vidal, Marc

doi:10.1126/science.1158684

Cited by 1,320 publications

(1,540 citation statements)

References 46 publications

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“…Our knowledge of these networks is very limited, for example, 80% of the molecular interactions in cells of Yeast [10] and 99.7% of human [11,12] are still unknown. Instead of blindly checking all possible interactions, to predict based on known interactions and focus on those links most likely to exist can sharply reduce the experimental costs if the predictions are accurate enough.…”

Section: Introductionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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Section: Introductionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,383

1,621

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“…Despite the clear success of large-scale interaction discovery initiatives, which have revealed thousands of unexpected interactions, the poor overlap observed between different screens run on the same organism [8,15] promoted the conception that their results are too dirty to be applicable. It has taken the community a decade of intense research to retaliate this notion and finally, recent studies have provided a general framework to correctly interpret the outcome of high-throughput interaction discovery experiments [16,17]. Today, protein interaction maps are of enough quality to become very valuable tools to help in the understanding of the underlying mechanisms of life, and we anticipate that they will play a pivotal role in the future of biological and biomedical research.…”

Section: Introductionmentioning

confidence: 99%

A network medicine approach to human disease

2009

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Edited by Miguel De la Rosa Keywords:Network medicine Protein-protein interaction Protein network Drug discovery a b s t r a c t High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.

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“…This layer of the interactome is being understood through the mathematical modelling of how proteins in the cell interact to form signalling pathways and networks. In turn, the data for such mathematical modelling are being generated by large-scale proteinprotein interaction studies which, while currently focused almost exclusively on the yeast interactome (Yu et al, 2008) and other higher eukaryotes such as Caenorhabditis elegans (Li et al, 2004), will be applied to more fungi as the techniques involved become more widely adopted. Two general high-throughput approaches are currently used to study how proteins in the cell interact: determination of interacting protein partners following the purification from a cell extract of a known protein (the pull-down approach: Einarson, 2001); and yeast two-hybrid assays involving reporter gene expression (the binary approach: Fields & Song, 1989).…”

Section: Analysis Of the Fungal Proteomementioning

confidence: 99%

Fungal physiology – a future perspective

Wilson

Talbot

2009

Microbiology

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The study of fungal physiology is set to change dramatically in the next few years as highly scalable technologies are deployed allowing accurate measurement and identification of metabolites, proteins and transcripts within cells. The advent of next-generation DNA-sequencing technologies will also provide genome sequence information from large numbers of industrially relevant and pathogenic fungal species, and allow comparative genome analysis between strains and populations of fungi. When coupled with advances in gene functional analysis, proteinprotein interaction studies, live cell imaging and mathematical modelling, this promises a stepchange in our understanding of how fungal cells operate as integrated dynamic living systems.

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High-Quality Binary Protein Interaction Map of the Yeast Interactome Network

Abstract: High-Quality Binary Protein Interaction Map of the www.sciencemag.org (this information is current as of October 28, 2009 ):The following resources related to this article are available online at

Cited by 1,320 publications

References 46 publications

Link prediction in complex networks: A survey

Link prediction in complex networks: A survey

A network medicine approach to human disease

Fungal physiology – a future perspective

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