Results 31 to 40 of about 7,142,455 (272)

Testing for treatment effects on gene ontology [PDF]

BMC Bioinformatics, 2008
In studies that use DNA arrays to assess changes in gene expression, it is preferable to measure the significance of treatment effects on a group of genes from a pathway or functional category such as gene ontology terms (GO terms, http://www ...
Delongchamp Robert R, Reis Robert, Velasco Cruz, Desai Varsha G, Lee Taewon   +4 more
doaj   +4 more sources

The Gene Ontology's Reference Genome Project: a unified framework for functional annotation across species. [PDF]

PLoS Computational Biology, 2009
The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim ...
Reference Genome Group of the Gene Ontology Consortium
doaj   +1 more source

Bringing ontology to the Gene Ontology [PDF]

Comparative and Functional Genomics, 2003
AbstractWe present an analysis of some considerations involved in expressing the Gene Ontology (GO) as a machine‐processible ontology, reflecting principles of formal ontology. GO is a controlled vocabulary that is intended to facilitate communication between biologists by standardizing usage of terms in database annotations.
Jennifer Williams, William Andersen
openaire   +3 more sources

The Gene Ontology Categorizer [PDF]

Bioinformatics, 2004
Abstract Summary: The Gene Ontology Categorizer, developed jointly by the Los Alamos National Laboratory and Procter & Gamble Corp., provides a capability for the categorization task in the Gene Ontology (GO): given a list of genes of interest, what are the best nodes of the GO to summarize or categorize that list?
Andy Fulmer, Susan M. Mniszewski, Cliff Joslyn, Gary Gordon Heaton   +3 more
openaire   +3 more sources

On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report. [PDF]

PLoS Computational Biology, 2012
A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the "functional similarity" between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results.
Paul D Thomas, Valerie Wood, Christopher J Mungall, Suzanna E Lewis, Judith A Blake, Gene Ontology Consortium   +5 more
doaj   +1 more source

The Gene Ontology resource: enriching a GOld mine

Nucleic Acids Res., 2020
The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years.
S. Carbon, E. Douglass, Benjamin M. Good, D. Unni, N. Harris, C. Mungall, Siddartha Basu, R. Chisholm, R. Dodson, E. Hartline, P. Fey, P. Thomas, Laurent-Philippe Albou, Dustin Ebert, Michael J. Kesling, H. Mi, A. Muruganujan, Xiaosong Huang, Tremayne Mushayahama, Sandra A. LaBonte, D. Siegele, Giulia Antonazzo, H. Attrill, N. Brown, P. Garapati, Steven J. Marygold, V. Trovisco, G. D. Santos, K. Falls, C. Tabone, Pinglei Zhou, J. Goodman, V. Strelets, Jim Thurmond, Penelope Garmiri, Rizwan Ishtiaq, M. Rodríguez-López, M. Acencio, Martin Kuiper, A. Lægreid, C. Logie, R. Lovering, B. Kramarz, Shirin C. C. Saverimuttu, S. M. Pinheiro, Heather Gunn, Renzhi Su, Kate E. Thurlow, M. Chibucos, M. Giglio, Suvarna Nadendla, James B. Munro, Rebecca C. Jackson, M. Duesbury, N. del-Toro, B. Meldal, K. Paneerselvam, L. Perfetto, P. Porras, S. Orchard, A. Shrivastava, Hsin-Yu Chang, R. Finn, A. Mitchell, N. Rawlings, Lorna J. Richardson, A. Sangrador-Vegas, J. Blake, K. Christie, M. Dolan, H. Drabkin, D. Hill, L. Ni, D. Sitnikov, M. Harris, S. Oliver, Kim M Rutherford, V. Wood, Jaqueline Hayles, J. Bähler, E. Bolton, Jeffrey DePons, M. Dwinell, G. Hayman, M. Kaldunski, A. Kwitek, S. Laulederkind, Cody Plasterer, M. Tutaj, M. Vedi, Shur-Jen Wang, P. D’Eustachio, L. Matthews, J. Balhoff, Suzi A Aleksander, M. J. Alexander, J. Cherry, S. Engel, F. Gondwe, K. Karra, S. Miyasato, R. Nash, Matt Simison, M. Skrzypek, S. Weng, E. Wong, M. Feuermann, P. Gaudet, A. Morgat, E. Bakker, T. Berardini, L. Reiser, Shabari Subramaniam, E. Huala, C. Arighi, A. Auchincloss, K. Axelsen, Ghislaine Argoud-Puy, A. Bateman, M. Blatter, E. Boutet, Emily Bowler, L. Breuza, A. Bridge, R. Britto, Hema Bye-A-Jee, Cristina Casals-Casas, E. Coudert, P. Denny, A. Estreicher, M. Famiglietti, G. Georghiou, A. Gos, N. Gruaz-Gumowski, E. Hatton-Ellis, C. Hulo, A. Ignatchenko, F. Jungo, K. Laiho, P. L. Mercier, D. Lieberherr, A. Lock, Yvonne Lussi, Alistair MacDougall, M. Magrane, M. Martin, P. Masson, D. Natale, Nevila Hyka-Nouspikel, I. Pedruzzi, L. Pourcel, S. Poux, Sangya Pundir, C. Rivoire, Elena Speretta, S. Sundaram, N. Tyagi, Kate Warner, Rossana Zaru, Cathy H. Wu, A. Diehl, J. Chan, Christian A. Grove, Raymond Y. N. Lee, Hans-Michael Müller, Daniela Raciti, K. V. Auken, P. Sternberg, M. Berriman, M. Paulini, K. Howe, Sibyl Gao, Adam J. Wright, L. Stein, D. Howe, S. Toro, M. Westerfield, P. Jaiswal, L. Cooper, J. Elser   +179 more
semanticscholar   +1 more source

Gene Ontology Annotations and Resources [PDF]

Nucleic Acids Research, 2012
The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations.
Gene Ontology Consortium, Blake, JA, Dolan, M, Drabkin, H, Hill, DP, Li, Ni, Sitnikov, D, Bridges, S, Burgess, S, Buza, T, McCarthy, F, Peddinti, D, Pillai, L, Carbon, S, Dietze, H, Ireland, A, Lewis, SE, Mungall, CJ, Gaudet, P, Chrisholm, RL, Fey, P, Kibbe, WA, Basu, S, Siegele, DA, McIntosh, BK, Renfro, DP, Zweifel, AE, Hu, JC, Brown, NH, Tweedie, S, Alam-Faruque, Y, Apweiler, R, Auchinchloss, A, Axelsen, K, Bely, B, Blatter, M-C, Bonilla, C, Bouguerleret, L, Boutet, E, Breuza, L, Bridge, A, Chan, WM, Chavali, G, Coudert, E, Dimmer, E, Estreicher, A, Famiglietti, L, Feuermann, M, Gos, A, Gruaz-Gumowski, N, Hieta, R, Hinz, C, Hulo, C, Huntley, R, James, J, Jungo, F, Keller, G, Laiho, K, Legge, D, Lemercier, P, Lieberherr, D, Magrane, M, Martin, MJ, Masson, P, Mutowo-Muellenet, P, O'Donovan, C, Pedruzzi, I, Pichler, K, Poggioli, D, Porras Millán, P, Poux, S, Rivoire, C, Roechert, B, Sawford, T, Schneider, M, Stutz, A, Sundaram, S, Tognolli, M, Xenarios, I, Foulgar, R, Lomax, J, Roncaglia, P, Khodiyar, VK, Lovering, RC, Talmud, PJ, Chibucos, M, Giglio, M Gwinn, Chang, H-Y, Hunter, S, McAnulla, C, Mitchell, A, Sangrador, A, Stephan, R, Harris, MA, Oliver, SG, Rutherford, K, Wood, V, Bahler, J, Lock, A, Kersey, PJ, McDowall, DM, Staines, DM, Dwinell, M, Shimoyama, M, Laulederkind, S, Hayman, T, Wang, S-J, Petri, V, Lowry, T, D'Eustachio, P, Matthews, L, Balakrishnan, R, Binkley, G, Cherry, JM, Costanzo, MC, Dwight, SS, Engel, SR, Fisk, DG, Hitz, BC, Hong, EL, Karra, K, Miyasato, SR, Nash, RS, Park, J, Skrzypek, MS, Weng, S, Wong, ED, Berardini, TZ, Huala, E, Mi, H, Thomas, PD, Chan, J, Kishore, R, Sternberg, P, Van Auken, K, Howe, D, Westerfield, M   +136 more
openaire   +6 more sources

The Gene Ontology resource: enriching a GOld mine. [PDF]

, 2021
The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years.
Gene Ontology Consortium
core   +1 more source

Ontologizing gene-expression microarray data: characterizing clusters with Gene Ontology [PDF]

Bioinformatics, 2004
Abstract Summary: An XML-based Java application is described that provides a function-oriented overview of the results of cluster analysis of gene-expression microarray data based on Gene Ontology terms and associations. The application generates one HTML page with listings of the frequencies of explicit and implicit Gene Ontology ...
Peter N. Robinson, Andreas Wollstein, Ulrike Böhme, Brad Beattie   +3 more
openaire   +3 more sources

Gene Expression Analysis to Mine Highly Relevant Gene Data in Chronic Diseases and Annotating its GO Terms [PDF]

EAI Endorsed Transactions on Energy Web, 2020
Gene Expression Analysis seeks to find the highly expressive genes from a highly dimensional Microarray disease gene Database by using some statistical gene selection approaches based on supervised or unsupervised learning.
J. Bell, S. Vigila
doaj   +1 more source