The Experts below are selected from a list of 76614 Experts worldwide ranked by ideXlab platform
Jiajie Peng - One of the best experts on this subject based on the ideXlab platform.
-
improving the measurement of semantic similarity by combining Gene Ontology and co functional network a random walk based approach
BMC Systems Biology, 2018Co-Authors: Jiajie Peng, Xuanshuo Zhang, Weiwei Hui, Shuhui Liu, Xuequn ShangAbstract:Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based Gene semantic similarity has been effectively used to model Gene-to-Gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and Gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based Gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based Gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global Gene-to-Gene interactions in the co-functional network, especially on the species that Gene annotations in GO are far from complete.
-
improving the measurement of semantic similarity by combining Gene Ontology and co functional network a random walk based approach
BMC Systems Biology, 2018Co-Authors: Jiajie Peng, Xuanshuo Zhang, Junya Lu, Qianqian Li, Xuequn ShangAbstract:Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based Gene semantic similarity has been effectively used to model Gene-to-Gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and Gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based Gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based Gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global Gene-to-Gene interactions in the co-functional network, especially on the species that Gene annotations in GO are far from complete.
-
Improving the measurement of semantic similarity by combining Gene Ontology and co-functional network: a random walk based approach
BMC, 2018Co-Authors: Jiajie Peng, Xuanshuo Zhang, Weiwei Hui, Shuhui Liu, Xuequn ShangAbstract:Abstract Background Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based Gene semantic similarity has been effectively used to model Gene-to-Gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and Gene annotations. Results We present NETSIM2, a new network-based method that allows researchers to measure GO-based Gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based Gene functional similarity. Conclusions Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global Gene-to-Gene interactions in the co-functional network, especially on the species that Gene annotations in GO are far from complete
-
measuring semantic similarities by combining Gene Ontology annotations and Gene co function networks
BMC Bioinformatics, 2015Co-Authors: Jiajie Peng, Sahra Uygun, Taehyong Kim, Yadong Wang, Seung Y Rhee, Jin ChenAbstract:Background Gene Ontology (GO) has been used widely to study functional relationships between Genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of Genes that are annotated to GO in most organisms.
Judith A Blake - One of the best experts on this subject based on the ideXlab platform.
-
Gene Ontology consortium going forward
Nucleic Acids Research, 2015Co-Authors: Judith A Blake, Juancarlos Chan, Ranjana Kishore, Paul W Sternberg, K Van Auken, Hansmichael Muller, James Done, Yanpeng LiAbstract:The Gene Ontology (GO; http://www.GeneOntology.org) is a community-based bioinformatics resource that supplies information about Gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the Ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the Ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for Generating Ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to Gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology.
-
the Gene Ontology enhancements for 2011
Nucleic Acids Research, 2012Co-Authors: Judith A Blake, D. Sitnikov, T. Buza, Harold Drabkin, D. P. Hill, Li Ni, Mary E Dolan, Shane C Burgess, Cathy R Gresham, Fiona M MccarthyAbstract:The Gene Ontology (GO) (http://www.GeneOntology.org) is a community bioinformatics resource that represents Gene product function through the use of structured, controlled vocabularies. The number of GO annotations of Gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted Ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the Ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.
-
Gene Ontology annotations: what they mean and where they come from
BMC Bioinformatics, 2008Co-Authors: D. P. Hill, Barry Smith, Monica S Mcandrews-hill, Judith A BlakeAbstract:To address the challenges of information integration and retrieval, the computational genomics community increasingly has come to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). Here we address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations, in the hope that this will lead both to better representations of biological reality through annotation and Ontology development and to more informed use of GO resources by experimental scientists.
-
a short study on the success of the Gene Ontology
Social Science Research Network, 2004Co-Authors: Michael Bada, Judith A Blake, Midori A Harris, Michael Ashburner, Michael J Cherry, Robert Stevens, Carole Goble, Yolanda Gil, Suzanna E LewisAbstract:While most ontologies have been used only by the groups who created them and for their initially defined purposes, the Gene Ontology (GO), an evolving structured controlled vocabulary of nearly 16,000 terms in the domain of biological functionality, has been widely used for annotation of biological-database entries and in biomedical research. As a set of learned lessons offered to other Ontology developers, we list and briefly discuss the characteristics of GO that we believe are most responsible for its success: community involvement; clear goals; limited scope; simple, intuitive structure; continuous evolution; active curation; and early use.
-
Gene Ontology tool for the unification of biology the Gene Ontology consortium
Nature Genetics, 2000Co-Authors: Michael Ashburner, Allan P. Davis, Selina S Dwight, Judith A Blake, Kara Dolinski, David Botstein, Catherine A Ball, Heather Butler, Michael J Cherry, Janan T EppigAbstract:Genomic sequencing has made it clear that a large fraction of the Genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of Gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.GeneOntology.org) are being constructed: biological process, molecular function and cellular component.
Jane Lomax - One of the best experts on this subject based on the ideXlab platform.
-
representing virus host interactions and other multi organism processes in the Gene Ontology
BMC Microbiology, 2015Co-Authors: Rebecca E Foulger, David Osumisutherland, Brenley K Mcintosh, Chantal Hulo, Patrick Masson, Sylvain Poux, Le P Mercier, Jane LomaxAbstract:Background The Gene Ontology project is a collaborative effort to provide descriptions of Gene products in a consistent and computable language, and in a species-independent manner. The Gene Ontology is designed to be applicable to all organisms but up to now has been largely under-utilized for prokaryotes and viruses, in part because of a lack of appropriate Ontology terms.
-
Genetic resources for advanced biofuel production described with the Gene Ontology
Frontiers in Microbiology, 2014Co-Authors: Trudy Tortoalalibo, Endang Purwantini, Jane Lomax, Joao C Setubal, Biswarup Mukhopadhyay, Brett M TylerAbstract:Dramatic increases in research in the area of microbial biofuel production coupled with high-throughput data Generation on bioenergy-related microbes has led to a deluge of information in the scientific literature and in databases. Consolidating this information and making it easily accessible requires a unified vocabulary. The Gene Ontology (GO) fulfills that requirement, as it is a well-developed structured vocabulary that describes the activities and locations of Gene products in a consistent manner across all kingdoms of life. The Microbial Energy Gene Ontology (MENGO: http://www.mengo.biochem.vt.edu) project is extending the GO to include new terms to describe microbial processes of interest to bioenergy production. Our effort has added over 600 bioenergy related terms to the Gene Ontology. These terms will aid in the comprehensive annotation of Gene products from diverse energy-related microbial genomes. An area of microbial energy research that has received a lot of attention is microbial production of advanced biofuels. These include alcohols such as butanol, isopropanol, isobutanol, and fuels derived from fatty acids, isoprenoids, and polyhydroxyalkanoates. These fuels are superior to first Generation biofuels (ethanol and biodiesel esterified from vegetable oil or animal fat), can be Generated from non-food feedstock sources, can be used as supplements or substitutes for gasoline, diesel and jet fuels, and can be stored and distributed using existing infrastructure. Here we review the roles of Genes associated with synthesis of advanced biofuels, and at the same time introduce the use of the GO to describe the functions of these Genes in a standardized way.
-
cross product extensions of the Gene Ontology
Journal of Biomedical Informatics, 2011Co-Authors: Christopher J Mungall, D. P. Hill, Midori A Harris, Michael Bada, Tanya Z Berardini, Jennifer I Deegan, Amelia Ireland, Jane LomaxAbstract:The Gene Ontology (GO) consists of nearly 30,000 classes for describing the activities and locations of Gene products. Manual maintenance of Ontology of this size is a considerable effort, and errors and inconsistencies inevitably arise. Reasoners can be used to assist with Ontology development, automatically placing classes in a subsumption hierarchy based on their properties. However, the historic lack of computable definitions within the GO has prevented the user of these tools. In this paper, we present preliminary results of an ongoing effort to normalize the GO by explicitly stating the definitions of compositional classes in a form that can be used by reasoners. These definitions are partitioned into mutually exclusive cross-product sets, many of which reference other OBO Foundry candidate ontologies for chemical entities, proteins, biological qualities and anatomical entities. Using these logical definitions we are gradually beginning to automate many aspects of Ontology development, detecting errors and filling in missing relationships. These definitions also enhance the GO by weaving it into the fabric of a wider collection of interoperating ontologies, increasing opportunities for data integration and enhancing genomic analyses.
-
cross product extensions of the Gene Ontology
Nature Precedings, 2009Co-Authors: Christopher J Mungall, D. P. Hill, Midori A Harris, Michael Bada, Tanya Z Berardini, Jennifer I Deegan, Amelia Ireland, Jane LomaxAbstract:The Gene Ontology is being normalized and extended to include computable logical definitions. These definitions are partitioned into mutually exclusive cross-product sets, many of which reference other OBO Foundry ontologies. The results can be used to reason over the Ontology, and to make cross-Ontology queries.
-
mapping the Gene Ontology into the unified medical language system
Comparative and Functional Genomics, 2004Co-Authors: Jane Lomax, Alexa T MccrayAbstract:We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine's Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating Gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems. After some initial explorations and discussions between the UMLS and GO teams, the GO was integrated with the UMLS. Overall, a total of 23% of the GO terms either matched directly (3%) or linked (20%) to existing UMLS concepts. All GO terms now have a corresponding, official UMLS concept, and the entire vocabulary is available through the web-based UMLS Knowledge Source Server. The mapping of the Gene Ontology, with its focus on structures, processes and functions at the molecular level, to the existing broad coverage UMLS should contribute to linking the language and practices of clinical medicine to the language and practices of genomics.
Xuequn Shang - One of the best experts on this subject based on the ideXlab platform.
-
improving the measurement of semantic similarity by combining Gene Ontology and co functional network a random walk based approach
BMC Systems Biology, 2018Co-Authors: Jiajie Peng, Xuanshuo Zhang, Weiwei Hui, Shuhui Liu, Xuequn ShangAbstract:Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based Gene semantic similarity has been effectively used to model Gene-to-Gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and Gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based Gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based Gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global Gene-to-Gene interactions in the co-functional network, especially on the species that Gene annotations in GO are far from complete.
-
improving the measurement of semantic similarity by combining Gene Ontology and co functional network a random walk based approach
BMC Systems Biology, 2018Co-Authors: Jiajie Peng, Xuanshuo Zhang, Junya Lu, Qianqian Li, Xuequn ShangAbstract:Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based Gene semantic similarity has been effectively used to model Gene-to-Gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and Gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based Gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based Gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global Gene-to-Gene interactions in the co-functional network, especially on the species that Gene annotations in GO are far from complete.
-
Improving the measurement of semantic similarity by combining Gene Ontology and co-functional network: a random walk based approach
BMC, 2018Co-Authors: Jiajie Peng, Xuanshuo Zhang, Weiwei Hui, Shuhui Liu, Xuequn ShangAbstract:Abstract Background Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based Gene semantic similarity has been effectively used to model Gene-to-Gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and Gene annotations. Results We present NETSIM2, a new network-based method that allows researchers to measure GO-based Gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based Gene functional similarity. Conclusions Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global Gene-to-Gene interactions in the co-functional network, especially on the species that Gene annotations in GO are far from complete
Alexa T Mccray - One of the best experts on this subject based on the ideXlab platform.
-
mapping the Gene Ontology into the unified medical language system
Comparative and Functional Genomics, 2004Co-Authors: Jane Lomax, Alexa T MccrayAbstract:We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine's Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating Gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems. After some initial explorations and discussions between the UMLS and GO teams, the GO was integrated with the UMLS. Overall, a total of 23% of the GO terms either matched directly (3%) or linked (20%) to existing UMLS concepts. All GO terms now have a corresponding, official UMLS concept, and the entire vocabulary is available through the web-based UMLS Knowledge Source Server. The mapping of the Gene Ontology, with its focus on structures, processes and functions at the molecular level, to the existing broad coverage UMLS should contribute to linking the language and practices of clinical medicine to the language and practices of genomics.
-
mapping the Gene Ontology into the unified medical language system research papers
Comparative and Functional Genomics, 2004Co-Authors: Jane Lomax, Alexa T MccrayAbstract:We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine's Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating Gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems. After some initial explorations and discussions between the UMLS and GO teams, the GO was integrated with the UMLS. Overall, a total of 23p of the GO terms either matched directly (3p) or linked (20p) to existing UMLS concepts. All GO terms now have a corresponding, official UMLS concept, and the entire vocabulary is available through the web-based UMLS Knowledge Source Server. The mapping of the Gene Ontology, with its focus on structures, processes and functions at the molecular level, to the existing broad coverage UMLS should contribute to linking the language and practices of clinical medicine to the language and practices of genomics. Copyright © 2004 John Wiley & Sons, Ltd.