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207
Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae
- Mol. Cell. Proteomics
, 2007
"... Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, howeve ..."
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Cited by 137 (1 self)
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Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two
Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps
- BIOINFORMATICS, VOL. 21 SUPPL. 1 2005, PAGES I302–I310
, 2005
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Semi-supervised graph clustering: a kernel approach
, 2008
"... Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this ..."
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Cited by 94 (3 self)
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Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We first show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective (Dhillon et al., in Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004a). A recent theoretical connection between weighted kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For graph data, this result leads to algorithms for optimizing several new semi-supervised graph clustering objectives. For vector data, the kernel approach also enables us to find clusters with non-linear boundaries in the input data space. Furthermore, we show that recent work on spectral learning (Kamvar et al., in Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2003) may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current state-of-the-art semi-supervised algorithms on both vector-based and graph-based data sets.
Assessing the limits of genomic data integration for predicting protein networks
, 2005
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Prediction of functional modules based on comparative genome analysis and Gene Ontology application
- Nucleic Acids Res
, 2005
"... We present a computational method for the prediction of functional modules encoded in microbial genomes. In this work, we have also developed a formal measure to quantify the degree of consistency between the predicted and the known modules, and have carried out statistical significance analysis of ..."
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Cited by 36 (8 self)
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We present a computational method for the prediction of functional modules encoded in microbial genomes. In this work, we have also developed a formal measure to quantify the degree of consistency between the predicted and the known modules, and have carried out statistical significance analysis of consist-ency measures. We first evaluate the functional rela-tionship between two genes from three different perspectives—phylogenetic profile analysis, gene neighborhood analysis and Gene Ontology assign-ments. We then combine the three different sources of information in the frameworkofBayesian inference, and we use the combined information to measure the strength of gene functional relationship. Finally, we apply a threshold-based method to predict functional modules. By applying this method to Escherichia coli K12, we have predicted 185 functional modules. Our predictions are highly consistent with the previously known functional modules in E.coli. The application results have demonstrated that our approach ishighly promising for the prediction of functional modules encoded in a microbial genome.
Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Res
, 2009
"... comprehensive data integration Global networks of functional coupling in eukaryotes from ..."
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Cited by 34 (8 self)
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comprehensive data integration Global networks of functional coupling in eukaryotes from
A large-scale evaluation of computational protein function prediction.
- Nat. Methods
, 2013
"... AnAlysis nAture methods | ADVANCE ONLINE PUBLICATION | Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. if computational predictions are to be relied upon, ..."
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Cited by 31 (2 self)
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AnAlysis nAture methods | ADVANCE ONLINE PUBLICATION | Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. if computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from organisms. two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools. The accurate annotation of protein function is key to understanding life at the molecular level and has great biomedical and pharmaceutical implications. However, with its inherent difficulty and expense, experimental characterization of function cannot scale up to accommodate the vast amount of sequence data already available 1 . The computational annotation of protein function has therefore emerged as a problem at the forefront of computational and molecular biology. Many solutions have been proposed in the last four decades 2-10 , yet the task of computational functional inference in a laboratory often relies on traditional approaches such as identifying domains or finding Basic Local Alignment Search Tool (BLAST) 11 hits among proteins with experimentally determined function. Recently, the availability of genomic-level sequence information for thousands of species, coupled with massive high-throughput experimental data, has created new opportunities for function prediction. A large number of methods have been proposed to exploit these data, including function prediction from amino acid sequence
Semidefinite programming
- Interior Point Methods of Mathematical Programming
, 1996
"... Alignment of molecular networks by integer quadratic ..."
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Cited by 28 (2 self)
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Alignment of molecular networks by integer quadratic
A scalable method for integration and functional analysis of multiple microarray datasets
- Bioinformatics
, 2006
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