The Experts below are selected from a list of 610512 Experts worldwide ranked by ideXlab platform
Hans H. Stærfeldt - One of the best experts on this subject based on the ideXlab platform.
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A scored human Protein-Protein Interaction network to catalyze genomic interpretation.
Nature methods, 2016Co-Authors: Rasmus Wernersson, Rasmus B. Hansen, Johnathan Mercer, Greg Slodkowicz, Kristoffer Rapacki, Heiko Horn, Christopher T Workman, Olga Rigina, Hans H. StærfeldtAbstract:Genome-scale human Protein-Protein Interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human Protein-Protein Interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more Interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.
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A scored human Protein-Protein Interaction network to catalyze genomic interpretation
Nature Methods, 2016Co-Authors: Taibo Li, Rasmus B. Hansen, Johnathan Mercer, Greg Slodkowicz, Kristoffer Rapacki, Heiko Horn, Christopher T Workman, Olga Rigina, Rasmus Wernersson, Hans H. StærfeldtAbstract:Human Protein-Protein Interaction networks are critical to understanding cell biology and interpreting genetic and genomic data, but are challenging to produce in individual large-scale experiments. We describe a general computational framework that through data integration and quality control provides a scored human Protein-Protein Interaction network (InWeb\_IM). Juxtaposed with five comparable resources, InWeb\_IM has 2.8 times more Interactions (~585K) and a superior functional signal showing that the added Interactions reflect real cellular biology. InWeb_IM is a versatile resource for accurate and cost-efficient functional interpretation of massive genomic datasets illustrated by annotating candidate genes from >4,700 cancer genomes and genes involved in neuropsychiatric diseases.
Xiaoyan Chen - One of the best experts on this subject based on the ideXlab platform.
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identifying Protein Protein Interaction sites using granularity computing of quotient space theory
Rough Sets and Knowledge Technology, 2010Co-Authors: Yan Ping Zhang, Yongcheng Wang, Xiaoyan ChenAbstract:The function of Protein-Protein Interaction is very important to cell activity. Studying Protein-Protein Interaction can help us understand life activities and pharmaceutical design. In this study, a kernel covering algorithm combined with the theory of granular computing of quotient space for predicting Protein-Protein Interaction sites is proposed, (i.e. KCA-GS Model). This method achieves good performances, and the Sensitivity, Specificity, Accuracy and Correlation coefficient are 52.97%, 53.92%, 70.27%, 24.61%, respectively. It is indicated that our method is effective, potential and promising to identify Protein-Protein Interaction sites.
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RSKT - Identifying Protein-Protein Interaction sites using granularity computing of quotient space theory
Lecture Notes in Computer Science, 2010Co-Authors: Yan Ping Zhang, Yongcheng Wang, Xiaoyan ChenAbstract:The function of Protein-Protein Interaction is very important to cell activity. Studying Protein-Protein Interaction can help us understand life activities and pharmaceutical design. In this study, a kernel covering algorithm combined with the theory of granular computing of quotient space for predicting Protein-Protein Interaction sites is proposed, (i.e. KCA-GS Model). This method achieves good performances, and the Sensitivity, Specificity, Accuracy and Correlation coefficient are 52.97%, 53.92%, 70.27%, 24.61%, respectively. It is indicated that our method is effective, potential and promising to identify Protein-Protein Interaction sites.
Aidong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Protein Interaction Networks: Computational Analysis
2009Co-Authors: Aidong ZhangAbstract:The analysis of Protein-Protein Interactions is fundamental to the understanding of cellular organization, processes, and functions. Recent large-scale investigations of Protein-Protein Interactions using such techniques as two-hybrid systems, mass spectrometry, and Protein microarrays have enriched the available Protein Interaction data and facilitated the construction of integrated Protein-Protein Interaction networks. The resulting large volume of Protein-Protein Interaction data has posed a challenge to experimental investigation. This book provides a comprehensive understanding of the computational methods available for the analysis of Protein-Protein Interaction networks. It offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods. The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research.
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semantic integration to identify overlapping functional modules in Protein Interaction networks
BMC Bioinformatics, 2007Co-Authors: Young-rae Cho, Woochang Hwang, Murali Ramanathan, Aidong ZhangAbstract:The systematic analysis of Protein-Protein Interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the Protein Interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable Interactions and the complex connectivity of the network. The integration of Protein-Protein Interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of Protein-Protein Interactions. The Protein Interaction networks can be converted into a weighted graph representation by assigning the reliability values to each Interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted Interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. The integration of Protein Interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.
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a seed refine algorithm for detecting Protein complexes from Protein Interaction data
IEEE Transactions on Nanobioscience, 2007Co-Authors: Pengjun Pei, Aidong ZhangAbstract:New technology advances in large-scale Protein-Protein Interaction detection provide researchers an initial view of Proteins on a global scale. These massive data sets provide a valuable source for elucidating the biomolecular mechanism in the cell. In this paper, we investigate the problem of Protein complex detection from noisy Protein Interaction data, i.e., finding the subsets of Proteins that are closely coupled via Protein Interactions. We identify the challenges and propose a "seed-refine" approach. We propose a novel statistically meaningful subgraph quality measure, a two-layer seeding heuristic to find good seeds, and a novel subgraph refinement method that controls the overlap between subgraphs. Experiments show the desirable properties of our subgraph quality measure and the effectiveness of our "seed-refine" algorithm
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BIBE - SIGN: reliable Protein Interaction identification by integrating the Similarity In GO and the similarity in Protein Interaction Networks
2007 IEEE 7th International Symposium on BioInformatics and BioEngineering, 2007Co-Authors: Woochang Hwang, Aidong Zhang, Taehyong Kim, Young-rae Cho, Murali RamanathanAbstract:High-throughput techniques for Protein-Protein Interaction detection in a genomic scale have provided us a genomic wide view of molecular Interactions of many living organisms. A few approaches were proposed to scrutinize Protein-Protein Interactions of living organisms. By the way, the binary nature of the current Protein Interaction data sets imposes challenges for effective analysis. Furthermore, their performance was suffered by the intrinsic defect, i.e., high noise level, of high-throughput data. This unpleasantly high false positive rate could lead many devoted researches to erroneous biological conclusions. We propose a novel reliability measurement for Protein Interactions integrating the similarity in gene ontology and the topological similarity in Protein Interaction networks. Our metric has been proven to be an effective reliability metric for identifying biologically more reliable Interactions through the analyses performed from various view points, e.g., functional homogeneity, subcellular localizational homogeneity, and gene expression correlation, etc.
Trey Ideker - One of the best experts on this subject based on the ideXlab platform.
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a proteome wide Protein Interaction map for campylobacter jejuni
Genome Biology, 2007Co-Authors: Jodi R Parrish, Guozhen Liu, Julie A Hines, Jason E Chan, Bernie A Mangiola, Huamei Zhang, Svetlana Pacifico, Farshad Fotouhi, Victor J Dirita, Trey IdekerAbstract:Background Data from large-scale Protein Interaction screens for humans and model eukaryotes have been invaluable for developing systems-level models of biological processes. Despite this value, only a limited amount of Interaction data is available for prokaryotes. Here we report the systematic identification of Protein Interactions for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide.
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efficient algorithms for detecting signaling pathways in Protein Interaction networks
Journal of Computational Biology, 2006Co-Authors: Jacob N Scott, Richard M Karp, Trey Ideker, Roded SharanAbstract:The interpretation of large-scale Protein network data depends on our ability to identify significant substructures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths and trees in graphs to the problem of identifying pathways in Protein Interaction networks. We present linear-time algorithms for finding paths and trees in networks under several biologically motivated constraints. We apply our methodology to search for Protein pathways in the yeast Protein-Protein Interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths and trees in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in about three hours.
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efficient algorithms for detecting signaling pathways in Protein Interaction networks
Research in Computational Molecular Biology, 2005Co-Authors: Jacob N Scott, Richard M Karp, Trey Ideker, Roded SharanAbstract:The interpretation of large-scale Protein network data depends on our ability to identify significant sub-structures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths in graphs to the problem of identifying pathways in Protein Interaction networks. We present linear-time algorithms for finding paths in networks under several biologically-motivated constraints. We apply our methodology to search for Protein pathways in the yeast Protein-Protein Interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in less than two hours.
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conserved patterns of Protein Interaction in multiple species
Proceedings of the National Academy of Sciences of the United States of America, 2005Co-Authors: Roded Sharan, Silpa Suthram, Ryan Kelley, Tanja Kuhn, Scott Mccuine, Peter Uetz, Taylor Sittler, Richard M Karp, Trey IdekerAbstract:To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available Protein–Protein Interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated Protein Interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed Protein functions and 2,609 previously undescribed Protein Interactions. We tested 60 Interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and Interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.
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PathBLAST: a tool for alignment of Protein Interaction networks.
Nucleic acids research, 2004Co-Authors: Brian P Kelley, Bingbing Yuan, Fran Lewitter, Roded Sharan, Brent R Stockwell, Trey IdekerAbstract:PathBLAST is a network alignment and search tool for comparing Protein Interaction networks across species to identify Protein pathways and complexes that have been conserved by evolution. The basic method searches for high-scoring alignments between pairs of Protein Interaction paths, for which Proteins of the first path are paired with putative orthologs occurring in the same order in the second path. This technique discriminates between true- and false-positive Interactions and allows for functional annotation of Protein Interaction pathways based on similarity to the network of another, well-characterized species. PathBLAST is now available at http://www.pathblast.org/ as a web-based query. In this implementation, the user specifies a short Protein Interaction path for query against a target Protein-Protein Interaction network selected from a network database. PathBLAST returns a ranked list of matching paths from the target network along with a graphical view of these paths and the overlap among them. Target Protein-Protein Interaction networks are currently available for Helicobacter pylori, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. Just as BLAST enables rapid comparison of Protein sequences between genomes, tools such as PathBLAST are enabling comparative genomics at the network level.
Rasmus Wernersson - One of the best experts on this subject based on the ideXlab platform.
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A scored human Protein-Protein Interaction network to catalyze genomic interpretation.
Nature methods, 2016Co-Authors: Rasmus Wernersson, Rasmus B. Hansen, Johnathan Mercer, Greg Slodkowicz, Kristoffer Rapacki, Heiko Horn, Christopher T Workman, Olga Rigina, Hans H. StærfeldtAbstract:Genome-scale human Protein-Protein Interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human Protein-Protein Interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more Interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.
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A scored human Protein-Protein Interaction network to catalyze genomic interpretation
Nature Methods, 2016Co-Authors: Taibo Li, Rasmus B. Hansen, Johnathan Mercer, Greg Slodkowicz, Kristoffer Rapacki, Heiko Horn, Christopher T Workman, Olga Rigina, Rasmus Wernersson, Hans H. StærfeldtAbstract:Human Protein-Protein Interaction networks are critical to understanding cell biology and interpreting genetic and genomic data, but are challenging to produce in individual large-scale experiments. We describe a general computational framework that through data integration and quality control provides a scored human Protein-Protein Interaction network (InWeb\_IM). Juxtaposed with five comparable resources, InWeb\_IM has 2.8 times more Interactions (~585K) and a superior functional signal showing that the added Interactions reflect real cellular biology. InWeb_IM is a versatile resource for accurate and cost-efficient functional interpretation of massive genomic datasets illustrated by annotating candidate genes from >4,700 cancer genomes and genes involved in neuropsychiatric diseases.
