The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Elizabeth Montague - One of the best experts on this subject based on the ideXlab platform.
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finding text supported gene to disease co appearances with Moped digger
Omics A Journal of Integrative Biology, 2015Co-Authors: Eugene Kolker, Roger Higdon, Elizabeth Stewart, John Choiniere, Elizabeth Montague, Imre Janko, Aaron Lai, Mary EckertAbstract:Gene/disease associations are a critical part of exploring disease causes and ultimately cures, yet the publications that might provide such information are too numerous to be manually reviewed. We present a software utility, Moped-Digger, that enables focused human assessment of literature by applying natural language processing (NLP) to search for customized lists of genes and diseases in titles and abstracts from biomedical publications. The results are ranked lists of gene/disease co-appearances and the publications that support them. Analysis of 18,159,237 PubMed title/abstracts yielded 1,796,799 gene/disease co-appearances that can be used to focus attention on the most promising publications for a possible gene/disease association. An integrated score is provided to enable assessment of broadly presented published evidence to capture more tenuous connections. Moped-Digger is written in Java and uses Apache Lucene 5.0 library. The utility runs as a command-line program with a variety of user-options and is freely available for download from the Moped 3.0 website (Moped.proteinspire.org).
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beyond protein expression Moped goes multi omics
Nucleic Acids Research, 2015Co-Authors: Elizabeth Montague, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, William BroomallAbstract:Moped (Multi-Omics Profiling Expression Database; http://Moped.proteinspire.org) has transitioned from solely a protein expression database to a multiomics resource for human and model organisms. Through a web-based interface, Moped presents consistently processed data for gene, protein and pathway expression. To improve data quality, consistency and use, Moped includes metadata detailing experimental design and analysis methods. The multi-omics data are integrated through direct links between genes and proteins and further connected to pathways and experiments. Moped now contains over 5 million records, information for approximately 75 000 genes and 50 000 proteins from four organisms (human, mouse, worm, yeast). These records correspond to 670 unique combinations of experiment, condition, localization and tissue. Moped includes the following new features: pathway expression, Pathway Details pages, experimental metadata checklists, experiment summary statistics and more advanced searching tools. Advanced searching enables querying for genes, proteins, experiments, pathways and keywords of interest. The system is enhanced with visualizations for comparing across different data types. In the future Moped will expand the number of organisms, increase integration with pathways and provide connections to disease.
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Moped 2 5 an integrated multi omics resource multi omics profiling expression database now includes transcriptomics data
Omics A Journal of Integrative Biology, 2014Co-Authors: Elizabeth Montague, Roger Higdon, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, Gregory Yandl, William BroomallAbstract:Abstract Multi-omics data-driven scientific discovery crucially rests on high-throughput technologies and data sharing. Currently, data are scattered across single omics repositories, stored in varying raw and processed formats, and are often accompanied by limited or no metadata. The Multi-Omics Profiling Expression Database (Moped, http://Moped.proteinspire.org) version 2.5 is a freely accessible multi-omics expression database. Continual improvement and expansion of Moped is driven by feedback from the Life Sciences Community. In order to meet the emergent need for an integrated multi-omics data resource, Moped 2.5 now includes gene relative expression data in addition to protein absolute and relative expression data from over 250 large-scale experiments. To facilitate accurate integration of experiments and increase reproducibility, Moped provides extensive metadata through the Data-Enabled Life Sciences Alliance (DELSA Global, http://delsaglobal.org) metadata checklist. Moped 2.5 has greatly increase...
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Moped enables discoveries through consistently processed proteomics data
Journal of Proteome Research, 2014Co-Authors: Roger Higdon, Elizabeth Stewart, Larissa Stanberry, Winston A Haynes, John Choiniere, Elizabeth MontagueAbstract:The Model Organism Protein Expression Database (Moped, http://Moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. Moped aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of Moped adds new estimates of protein abundance and concentration as well as relative (differential) expression data. Moped provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. Moped supports the Human Proteome Project’s efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. Moped supports a new omics metadata checklist to harmonize data integration, analysis, and use. Moped’s development is d...
Bernard Laumon - One of the best experts on this subject based on the ideXlab platform.
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risk factors for injury accidents among Moped and motorcycle riders
Accident Analysis & Prevention, 2012Co-Authors: Aurelie Moskal, Jeanlouis Martin, Bernard LaumonAbstract:OBJECTIVE: To study and quantify the effect of factors related to the riders of powered two-wheelers on the risk of injury accident involvement. METHODOLOGY: Based on national data held by the police from 1996 to 2005, we conducted a case-control study with responsibility for the accident as the event of interest. We estimated the odds ratios for accident responsibility. Making the hypothesis that the non-responsible riders in the study are representative of all the riders on the road, we thus identified risk factors for being responsible for injury accidents. The studied factors are age, gender, helmet wearing, alcohol consumption, validity of the subject's driving licence and for how long it has been held, the trip purpose and the presence of a passenger on the vehicle. Moped and motorcycle riders are analyzed separately, adjusting for the main characteristics of the accident. RESULTS: For both Moped and motorcycle riders, being male, not wearing a helmet, exceeding the legal limit for alcohol and travelling for leisure purposes increased the risk of accident involvement. The youngest and oldest users had a greater risk of accident involvement. The largest risk factor was alcohol, and we identified a dose-effect relationship between alcohol consumption and accident risk, with an estimated odds ratio of over 10 for motorcycle and Moped riders with a BAC of 2 g/l or over. Among motorcycle users, riders without a licence had twice the risk of being involved in an accident than those holding a valid licence. However, the number of years the rider had held a licence reduced the risk of accident involvement. One difference between Moped and motorcycle riders involved the presence of a passenger on the vehicle: while carrying a passenger increased the risk of being responsible for the accident among Moped riders, it protected against this risk among motorcycle riders. CONCLUSION: This analysis of responsibility has identified the major factors contributing to excess risk of injury accidents, some of which could be targeted by prevention programmes.
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risk factors for injury accidents among Moped and motorcycle riders
Accident Analysis & Prevention, 2012Co-Authors: Aurelie Moskal, Jeanlouis Martin, Bernard LaumonAbstract:OBJECTIVE: To study and quantify the effect of factors related to the riders of powered two-wheelers on the risk of injury accident involvement. METHODOLOGY: Based on national data held by the police from 1996 to 2005, we conducted a case-control study with responsibility for the accident as the event of interest. We estimated the odds ratios for accident responsibility. Making the hypothesis that the non-responsible riders in the study are representative of all the riders on the road, we thus identified risk factors for being responsible for injury accidents. The studied factors are age, gender, helmet wearing, alcohol consumption, validity of the subject's driving licence and for how long it has been held, the trip purpose and the presence of a passenger on the vehicle. Moped and motorcycle riders are analyzed separately, adjusting for the main characteristics of the accident. RESULTS: For both Moped and motorcycle riders, being male, not wearing a helmet, exceeding the legal limit for alcohol and travelling for leisure purposes increased the risk of accident involvement. The youngest and oldest users had a greater risk of accident involvement. The largest risk factor was alcohol, and we identified a dose-effect relationship between alcohol consumption and accident risk, with an estimated odds ratio of over 10 for motorcycle and Moped riders with a BAC of 2 g/l or over. Among motorcycle users, riders without a licence had twice the risk of being involved in an accident than those holding a valid licence. However, the number of years the rider had held a licence reduced the risk of accident involvement. One difference between Moped and motorcycle riders involved the presence of a passenger on the vehicle: while carrying a passenger increased the risk of being responsible for the accident among Moped riders, it protected against this risk among motorcycle riders. CONCLUSION: This analysis of responsibility has identified the major factors contributing to excess risk of injury accidents, some of which could be targeted by prevention programmes. Language: en
William Broomall - One of the best experts on this subject based on the ideXlab platform.
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beyond protein expression Moped goes multi omics
Nucleic Acids Research, 2015Co-Authors: Elizabeth Montague, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, William BroomallAbstract:Moped (Multi-Omics Profiling Expression Database; http://Moped.proteinspire.org) has transitioned from solely a protein expression database to a multiomics resource for human and model organisms. Through a web-based interface, Moped presents consistently processed data for gene, protein and pathway expression. To improve data quality, consistency and use, Moped includes metadata detailing experimental design and analysis methods. The multi-omics data are integrated through direct links between genes and proteins and further connected to pathways and experiments. Moped now contains over 5 million records, information for approximately 75 000 genes and 50 000 proteins from four organisms (human, mouse, worm, yeast). These records correspond to 670 unique combinations of experiment, condition, localization and tissue. Moped includes the following new features: pathway expression, Pathway Details pages, experimental metadata checklists, experiment summary statistics and more advanced searching tools. Advanced searching enables querying for genes, proteins, experiments, pathways and keywords of interest. The system is enhanced with visualizations for comparing across different data types. In the future Moped will expand the number of organisms, increase integration with pathways and provide connections to disease.
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Moped 2 5 an integrated multi omics resource multi omics profiling expression database now includes transcriptomics data
Omics A Journal of Integrative Biology, 2014Co-Authors: Elizabeth Montague, Roger Higdon, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, Gregory Yandl, William BroomallAbstract:Abstract Multi-omics data-driven scientific discovery crucially rests on high-throughput technologies and data sharing. Currently, data are scattered across single omics repositories, stored in varying raw and processed formats, and are often accompanied by limited or no metadata. The Multi-Omics Profiling Expression Database (Moped, http://Moped.proteinspire.org) version 2.5 is a freely accessible multi-omics expression database. Continual improvement and expansion of Moped is driven by feedback from the Life Sciences Community. In order to meet the emergent need for an integrated multi-omics data resource, Moped 2.5 now includes gene relative expression data in addition to protein absolute and relative expression data from over 250 large-scale experiments. To facilitate accurate integration of experiments and increase reproducibility, Moped provides extensive metadata through the Data-Enabled Life Sciences Alliance (DELSA Global, http://delsaglobal.org) metadata checklist. Moped 2.5 has greatly increase...
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Moped model organism protein expression database
Nucleic Acids Research, 2012Co-Authors: Eugene Kolker, Roger Higdon, Larissa Stanberry, Winston A Haynes, Dean Welch, William Broomall, Doron Lancet, Natali KolkerAbstract:Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (Moped, htttp://Moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. Moped is designed to simplify the comparison and sharing of proteomics data for the greater research community. Moped uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. Moped empowers users to visualize their own expression data and compare it with existing studies. Further, Moped links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of Moped contains over 43 000 proteins with at least one spectral match and more than 11 million high certainty spectra.
Larissa Stanberry - One of the best experts on this subject based on the ideXlab platform.
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beyond protein expression Moped goes multi omics
Nucleic Acids Research, 2015Co-Authors: Elizabeth Montague, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, William BroomallAbstract:Moped (Multi-Omics Profiling Expression Database; http://Moped.proteinspire.org) has transitioned from solely a protein expression database to a multiomics resource for human and model organisms. Through a web-based interface, Moped presents consistently processed data for gene, protein and pathway expression. To improve data quality, consistency and use, Moped includes metadata detailing experimental design and analysis methods. The multi-omics data are integrated through direct links between genes and proteins and further connected to pathways and experiments. Moped now contains over 5 million records, information for approximately 75 000 genes and 50 000 proteins from four organisms (human, mouse, worm, yeast). These records correspond to 670 unique combinations of experiment, condition, localization and tissue. Moped includes the following new features: pathway expression, Pathway Details pages, experimental metadata checklists, experiment summary statistics and more advanced searching tools. Advanced searching enables querying for genes, proteins, experiments, pathways and keywords of interest. The system is enhanced with visualizations for comparing across different data types. In the future Moped will expand the number of organisms, increase integration with pathways and provide connections to disease.
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Moped 2 5 an integrated multi omics resource multi omics profiling expression database now includes transcriptomics data
Omics A Journal of Integrative Biology, 2014Co-Authors: Elizabeth Montague, Roger Higdon, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, Gregory Yandl, William BroomallAbstract:Abstract Multi-omics data-driven scientific discovery crucially rests on high-throughput technologies and data sharing. Currently, data are scattered across single omics repositories, stored in varying raw and processed formats, and are often accompanied by limited or no metadata. The Multi-Omics Profiling Expression Database (Moped, http://Moped.proteinspire.org) version 2.5 is a freely accessible multi-omics expression database. Continual improvement and expansion of Moped is driven by feedback from the Life Sciences Community. In order to meet the emergent need for an integrated multi-omics data resource, Moped 2.5 now includes gene relative expression data in addition to protein absolute and relative expression data from over 250 large-scale experiments. To facilitate accurate integration of experiments and increase reproducibility, Moped provides extensive metadata through the Data-Enabled Life Sciences Alliance (DELSA Global, http://delsaglobal.org) metadata checklist. Moped 2.5 has greatly increase...
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Moped enables discoveries through consistently processed proteomics data
Journal of Proteome Research, 2014Co-Authors: Roger Higdon, Elizabeth Stewart, Larissa Stanberry, Winston A Haynes, John Choiniere, Elizabeth MontagueAbstract:The Model Organism Protein Expression Database (Moped, http://Moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. Moped aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of Moped adds new estimates of protein abundance and concentration as well as relative (differential) expression data. Moped provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. Moped supports the Human Proteome Project’s efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. Moped supports a new omics metadata checklist to harmonize data integration, analysis, and use. Moped’s development is d...
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Moped model organism protein expression database
Nucleic Acids Research, 2012Co-Authors: Eugene Kolker, Roger Higdon, Larissa Stanberry, Winston A Haynes, Dean Welch, William Broomall, Doron Lancet, Natali KolkerAbstract:Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (Moped, htttp://Moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. Moped is designed to simplify the comparison and sharing of proteomics data for the greater research community. Moped uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. Moped empowers users to visualize their own expression data and compare it with existing studies. Further, Moped links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of Moped contains over 43 000 proteins with at least one spectral match and more than 11 million high certainty spectra.
Imre Janko - One of the best experts on this subject based on the ideXlab platform.
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finding text supported gene to disease co appearances with Moped digger
Omics A Journal of Integrative Biology, 2015Co-Authors: Eugene Kolker, Roger Higdon, Elizabeth Stewart, John Choiniere, Elizabeth Montague, Imre Janko, Aaron Lai, Mary EckertAbstract:Gene/disease associations are a critical part of exploring disease causes and ultimately cures, yet the publications that might provide such information are too numerous to be manually reviewed. We present a software utility, Moped-Digger, that enables focused human assessment of literature by applying natural language processing (NLP) to search for customized lists of genes and diseases in titles and abstracts from biomedical publications. The results are ranked lists of gene/disease co-appearances and the publications that support them. Analysis of 18,159,237 PubMed title/abstracts yielded 1,796,799 gene/disease co-appearances that can be used to focus attention on the most promising publications for a possible gene/disease association. An integrated score is provided to enable assessment of broadly presented published evidence to capture more tenuous connections. Moped-Digger is written in Java and uses Apache Lucene 5.0 library. The utility runs as a command-line program with a variety of user-options and is freely available for download from the Moped 3.0 website (Moped.proteinspire.org).
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beyond protein expression Moped goes multi omics
Nucleic Acids Research, 2015Co-Authors: Elizabeth Montague, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, William BroomallAbstract:Moped (Multi-Omics Profiling Expression Database; http://Moped.proteinspire.org) has transitioned from solely a protein expression database to a multiomics resource for human and model organisms. Through a web-based interface, Moped presents consistently processed data for gene, protein and pathway expression. To improve data quality, consistency and use, Moped includes metadata detailing experimental design and analysis methods. The multi-omics data are integrated through direct links between genes and proteins and further connected to pathways and experiments. Moped now contains over 5 million records, information for approximately 75 000 genes and 50 000 proteins from four organisms (human, mouse, worm, yeast). These records correspond to 670 unique combinations of experiment, condition, localization and tissue. Moped includes the following new features: pathway expression, Pathway Details pages, experimental metadata checklists, experiment summary statistics and more advanced searching tools. Advanced searching enables querying for genes, proteins, experiments, pathways and keywords of interest. The system is enhanced with visualizations for comparing across different data types. In the future Moped will expand the number of organisms, increase integration with pathways and provide connections to disease.
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Moped 2 5 an integrated multi omics resource multi omics profiling expression database now includes transcriptomics data
Omics A Journal of Integrative Biology, 2014Co-Authors: Elizabeth Montague, Roger Higdon, Elizabeth Stewart, Larissa Stanberry, John Choiniere, Imre Janko, Elaine Lee, Nathaniel Anderson, Gregory Yandl, William BroomallAbstract:Abstract Multi-omics data-driven scientific discovery crucially rests on high-throughput technologies and data sharing. Currently, data are scattered across single omics repositories, stored in varying raw and processed formats, and are often accompanied by limited or no metadata. The Multi-Omics Profiling Expression Database (Moped, http://Moped.proteinspire.org) version 2.5 is a freely accessible multi-omics expression database. Continual improvement and expansion of Moped is driven by feedback from the Life Sciences Community. In order to meet the emergent need for an integrated multi-omics data resource, Moped 2.5 now includes gene relative expression data in addition to protein absolute and relative expression data from over 250 large-scale experiments. To facilitate accurate integration of experiments and increase reproducibility, Moped provides extensive metadata through the Data-Enabled Life Sciences Alliance (DELSA Global, http://delsaglobal.org) metadata checklist. Moped 2.5 has greatly increase...