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1. Supporting database annotations and beyond with the Evidence & Conclusion Ontology (ECO)

   Chibucos, M.C.; Nadendla, S.; Munro, J.B.; Mitraka, E.; Olley, D.; Vasilevsky, N.A.; Brush, M.H.; Giglio, M. (November 2016, CEUR workshop proceedings)

   The Evidence & Conclusion Ontology (ECO) is a community standard for summarizing evidence in scientific research in a controlled, structured way. Annotations at the world's most frequented biological databases (e.g. model organisms, UniProt, Gene Ontology) are supported using ECO terms. ECO describes evidence derived from experimental and computational methods, author statements curated from the literature, inferences drawn by curators, and other types of evidence. Here, we describe recent ECO developments and collaborations, most notably: (i) a new ECO website containing user documentation, up-to-date news, and visualization tools; (ii) improvements to the ontology structure; (iii) implementing logic via an ongoing collaboration with the Ontology for Biomedical Investigations (OBI); (iv) addition of numerous experimental evidence types; and (v) addition of new evidence classes describing computationally derived evidence. Due to its utility, popularity, and simplicity, ECO is now expanding into realms beyond the protein annotation community, for example the biodiversity and phenotype communities. As ECO continues to grow as a resource, we are seeking new users and new use cases, with the hope that ECO will continue to be a broadly used and easy-to-implement community standard for representing evidence in diverse biological applications. Feel free to visit two ECO-sponsored workshops at ICBO 2016 to learn more: 1. “An introduction to the Evidence and Conclusion Ontology and representing evidence in scientific research” and 2. “OBI-ECO Interactions & Evidence”. 

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2. Supporting database annotations and beyond with the Evidence & Conclusion Ontology (ECO)

   Chibucos, M.C.; Nadendla, S.; Munro, J.B.; Mitraka, E.; Olley, D.; Vasilevsky, N.A.; Brush, M.H.; Giglio, M. (November 2016, CEUR workshop proceedings)

   The Evidence & Conclusion Ontology (ECO) is a community standard for summarizing evidence in scientific research in a controlled, structured way. Annotations at the world's most frequented biological databases (e.g. model organisms, UniProt, Gene Ontology) are supported using ECO terms. ECO describes evidence derived from experimental and computational methods, author statements curated from the literature, inferences drawn by curators, and other types of evidence. Here, we describe recent ECO developments and collaborations, most notably: (i) a new ECO website containing user documentation, up-to-date news, and visualization tools; (ii) improvements to the ontology structure; (iii) implementing logic via an ongoing collaboration with the Ontology for Biomedical Investigations (OBI); (iv) addition of numerous experimental evidence types; and (v) addition of new evidence classes describing computationally derived evidence. Due to its utility, popularity, and simplicity, ECO is now expanding into realms beyond the protein annotation community, for example the biodiversity and phenotype communities. As ECO continues to grow as a resource, we are seeking new users and new use cases, with the hope that ECO will continue to be a broadly used and easy-to-implement community standard for representing evidence in diverse biological applications. Feel free to visit two ECO-sponsored workshops at ICBO 2016 to learn more: 1. “An introduction to the Evidence and Conclusion Ontology and representing evidence in scientific research” and 2. “OBI-ECO Interactions & Evidence”. 

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3. The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations. In Dessimoz, C. and Škunca, N (eds.), "The Gene Ontology Handbook".

   Chibucos, M.C.; Siegele, D.A.; Hu, J.C.; Giglio, M. (November 2016, Methods in molecular biology)

   The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientific study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made. 

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4. A Gated Recurrent Unit based architecture for recognizing ontology concepts from biological literature

   https://doi.org/10.1186/s13040-022-00310-0  

   Devkota, Pratik; Mohanty, Somya D.; Manda, Prashanti (September 2022, BioData Mining)

   Abstract BackgroundAnnotating scientific literature with ontology concepts is a critical task in biology and several other domains for knowledge discovery. Ontology based annotations can power large-scale comparative analyses in a wide range of applications ranging from evolutionary phenotypes to rare human diseases to the study of protein functions. Computational methods that can tag scientific text with ontology terms have included lexical/syntactic methods, traditional machine learning, and most recently, deep learning. ResultsHere, we present state of the art deep learning architectures based on Gated Recurrent Units for annotating text with ontology concepts. We use the Colorado Richly Annotated Full Text Corpus (CRAFT) as a gold standard for training and testing. We explore a number of additional information sources including NCBI’s BioThesauraus and Unified Medical Language System (UMLS) to augment information from CRAFT for increasing prediction accuracy. Our best model results in a 0.84 F1 and semantic similarity. ConclusionThe results shown here underscore the impact for using deep learning architectures for automatically recognizing ontology concepts from literature. The augmentation of the models with biological information beyond that present in the gold standard corpus shows a distinct improvement in prediction accuracy. 

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5. The Gene Ontology resource: enriching a GOld mine

   https://doi.org/10.1093/nar/gkaa1113  

   Carbon, Seth; Douglass, Eric; Good, Benjamin M; Unni, Deepak R; Harris, Nomi L; Mungall, Christopher J; Basu, Siddartha; Chisholm, Rex L; Dodson, Robert J; Hartline, Eric; et al (December 2020, Nucleic Acids Research)

   null (Ed.)

   Abstract 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. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations. 

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