Probabilistic Model of The Human Protein - Protein Interaction Network  

Oleh:

Rhodes, Daniel R. ; Tomlins, Scott A. ; Varambally, Sooryanarayana ; Mahavisno, Vasudeva ; Barrette, Terrence ; Kalyana-Sundaram, Shanker ; Ghosh, Debashis ; Pandey, Akhilesh ; Chinnaiyan, Arul M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Nature Biotechnology: The Science and Business of Biotechnology vol. 23 no. 8 (Agu. 2005), page 951-960.
Topik: network; probabilistic model; interaction network

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: NN9.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans, a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of knowm interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cance genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

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