The Experts below are selected from a list of 30864 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, Olga Rigina, Christopher T Workman, 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, Olga Rigina, Rasmus Wernersson, Christopher T Workman, 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.
Jason E Shoemaker - One of the best experts on this subject based on the ideXlab platform.
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a dual controllability analysis of influenza virus host protein protein Interaction Networks for antiviral drug target discovery
BMC Bioinformatics, 2019Co-Authors: Emily E Ackerman, John F Alcorn, Takeshi Hase, Jason E ShoemakerAbstract:Host factors of influenza virus replication are often found in key topological positions within Protein-Protein Interaction Networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein Networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell’s progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein Interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing Protein-Protein Interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.
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a dual controllability analysis of influenza virus host protein protein Interaction Networks for antiviral drug target discovery
bioRxiv, 2018Co-Authors: Emily E Ackerman, John F Alcorn, Takeshi Hase, Jason E ShoemakerAbstract:Host factors of influenza virus replication often are often found in key topological positions within Protein-Protein Interaction Networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here we complete a two-part controllability analysis of two protein Networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein Interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell.
Christopher T Workman - 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, Olga Rigina, Christopher T Workman, 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, Olga Rigina, Rasmus Wernersson, Christopher T Workman, 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.
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a random set scoring model for prioritization of disease candidate genes using protein complexes and data mining of generif omim and pubmed records
BMC Bioinformatics, 2014Co-Authors: Li Jiang, Christopher T Workman, Stefan M Edwards, Bo Thomsen, Bernt Guldbrandtsen, Peter SorensenAbstract:Background Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of Protein-Protein Interaction Networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.
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, Olga Rigina, Christopher T Workman, 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, Olga Rigina, Rasmus Wernersson, Christopher T Workman, 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.
Erliang Zeng - One of the best experts on this subject based on the ideXlab platform.
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integration of anatomy ontology data with protein protein Interaction Networks improves the candidate gene prediction accuracy for anatomical entities
BMC Bioinformatics, 2020Co-Authors: Pasan C Fernando, Paula M Mabee, Erliang ZengAbstract:BACKGROUND Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-Protein Interaction (PPI) Networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI Networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI Networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI Networks by reducing the number of false positive and false negative Interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene Networks by calculating semantic similarity between the genes. These anatomy-based gene Networks were semantic Networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene Networks with mouse and zebrafish PPI Networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. RESULTS According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated Networks, which were semantically improved PPI Networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI Networks for both zebrafish and mouse. CONCLUSION Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI Networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.
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integration of anatomy ontology data with protein protein Interaction Networks improves the candidate gene prediction accuracy for anatomical entities
bioRxiv, 2020Co-Authors: Pasan C Fernando, Paula M Mabee, Erliang ZengAbstract:Background: Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet-lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-Protein Interaction (PPI) Networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. This is because PPI Networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes for anatomical entities. We developed an integrative framework to predict candidate genes for anatomical entities by combining existing experimental knowledge about gene-anatomy relationships with PPI Networks using anatomy ontology annotations. We expected this integration to improve the quality of the PPI Networks and be better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomy entity annotations for zebrafish and mouse genes to construct gene Networks by calculating semantic similarity between the genes. These anatomy-based gene Networks are semantic Networks, as they are constructed based on the Uberon anatomy ontology annotations that are obtained from the experimental data in the literature. We integrated these anatomy-based gene Networks with mouse and zebrafish PPI Networks retrieved from the STRING database, and we compared the performance of their network-based candidate gene predictions. Results: According to candidate gene prediction performance evaluations tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated Networks showed better receiver operating characteristic (ROC) and precision-recall curve performances than PPI Networks for both zebrafish and mouse. Conclusion: Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI Networks via anatomy ontology improves the network quality, which makes them better optimized for predicting candidate genes for anatomical entities.
