The Experts below are selected from a list of 604158 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.
Degen Huang - One of the best experts on this subject based on the ideXlab platform.
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an approach to improve kernel based protein protein Interaction extraction by learning from large scale network data
Methods, 2015Co-Authors: Rui Guo, Zhenchao Jiang, Degen HuangAbstract:Abstract Protein–Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word representation techniques: distributed representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-Protein-Protein-Interaction-extraction-by-unsupervised-word-representation-codes-and-data.html .
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Improving Kernel-based Protein-Protein Interaction extraction by unsupervised word representation
2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014Co-Authors: Lishuang Li, Zhenchao Jiang, Degen HuangAbstract:As an important branch of biomedical information extraction, Protein-Protein Interaction extraction (PPIe) from biomedical literatures has been widely researched, and machine learning methods have achieved great success for this task. However, the word feature generally adopted in the existing methods suffers badly from vocabulary gap and data sparseness, weakening the classification performance. In this paper, the unsupervised word representation approach is introduced to address these problems. Three word representation methods are adopted to improve the performance of PPIe: distributed representation, vector clustering and Brown clusters representation. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora.
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.
Florian Klimm - One of the best experts on this subject based on the ideXlab platform.
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functional module detection through integration of single cell rna sequencing data with protein protein Interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein Interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein Interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein Interactions. We find that different transcriptional states have different subnetworks of the protein–protein Interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein Interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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functional module detection through integration of single cell rna sequencing data with protein protein Interaction networks
BMC Genomics, 2020Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:BACKGROUND Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. RESULTS In this study, we present SCPPIN, a method for integrating single-cell RNA sequencing data with Protein-Protein Interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted Protein-Protein Interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With SCPPIN we expand the output of differential expressed genes analysis with information from protein Interactions. We find that different transcriptional states have different subnetworks of the Protein-Protein Interaction networks significantly enriched which represent biological pathways. In these pathways, SCPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. CONCLUSIONS The introduced SCPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein Interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
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functional module detection through integration of single cell rna sequencing data with protein protein Interaction networks
bioRxiv, 2019Co-Authors: Florian Klimm, Enrique M Toledo, Thomas Monfeuga, Charlotte M. Deane, Fang Zhang, Gesine ReinertAbstract:Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with Protein-Protein Interaction networks to detect active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted Protein-Protein Interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein Interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but of crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.
Yuko Hara - One of the best experts on this subject based on the ideXlab platform.
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improved protein protein Interaction assay flimpia by the entrapment of luciferase conformation
Analytical Chemistry, 2014Co-Authors: Yuki Ohmuromatsuyama, Yuko HaraAbstract:Recently we reported a novel protein–protein Interaction assay FlimPIA (firefly luminescent intermediate-based protein–protein Interaction assay) based on the functional complementation of two mutant firefly luciferases (Fluc), each defective in its one of two half reactions. The assay detects approximation of two mutant Flucs, namely, a “Donor” that catalyzes ATP-driven luciferin adenylation to produce a luciferyl-adenylate intermediate, and an “Acceptor” that mainly catalyzes subsequent oxidative luminescent reaction. However, there was a problem in FlimPIA that the remaining adenylation activity of the Acceptor constituted its background signal and hampered its wider use. In this study, we aimed at reducing the background signal by trapping the Acceptor to the “oxidation” conformation, either chemically or by disulfide bonding. The results showed higher sensitivity and detection over the longer distance of the developed assay compared to conventional FlimPIA, Fluc-based protein-fragment complementation...
