ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts

Autor: Elizabeth T. Hobbs, Stephen M. Goralski, Ashley Mitchell, Andrew Simpson, Dorjan Leka, Emmanuel Kotey, Matt Sekira, James B. Munro, Suvarna Nadendla, Rebecca Jackson, Aitor Gonzalez-Aguirre, Martin Krallinger, Michelle Giglio, Ivan Erill

Publikováno v: Frontiers in Research Metrics and Analytics, Vol 6 (2021)

Analysis of high-throughput experiments in the life sciences frequently relies upon standardized information about genes, gene products, and other biological entities. To provide this information, expert curators are increasingly relying on text mini

Externí odkaz: https://doaj.org/article/6563012a49b34ad48518e463b4bcd94c

ECO, the Evidence & Conclusion Ontology: community standard for evidence information

Autor: Shoshannah Ball, Rebecca Tauber, Marcus C. Chibucos, Pascale Gaudet, Elizabeth T. Hobbs, Elvira Mitraka, Dustin Olley, Suvarna Nadendla, Christopher J. Mungall, Ivan Erill, Deborah A. Siegele, Michelle G. Giglio, James C. Hu, James B. Munro, Lynn M. Schriml

Publikováno v: Nucleic Acids Research
Nucleic acids research, vol 47, iss D1
Giglio, Michelle; Tauber, Rebecca; Nadendla, Suvarna; Munro, James; Olley, Dustin; Ball, Shoshannah; et al.(2019). ECO, the Evidence & Conclusion Ontology: community standard for evidence information.. Nucleic acids research, 47(D1), D1186-D1194. doi: 10.1093/nar/gky1036. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/8r16010f

The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe types of evidence and assertion methods. ECO terms are used in the process of biocuration to capture the evidence that supports biological assertions (e.g. gene product

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::464b95c07837d88ebb0c1ad1a9c113fa
http://europepmc.org/articles/PMC6323956

A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data

Autor: Talmo D. Pereira, Elizabeth T. Hobbs, Patrick K. O'Neill, Ivan Erill

Publikováno v: Algorithms for Molecular Biology : AMB

Background Metagenomics enables the analysis of bacterial population composition and the study of emergent population features, such as shared metabolic pathways. Recently, we have shown that metagenomics datasets can be leveraged to characterize pop

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c4dee36d1aab23660de1ec9e6e3c5a8