Biocuration is the process of analyzing biological or biomedical articles to organize biological data into data repositories using taxonomies and ontologies. Due to the expanding number of articles and the relatively small number of biocurators, automation is desired to improve the workflow of assessing articles worth curating. As figures convey essential information, automatically integrating images may improve curation. In this work, we instantiate and evaluate a first-in-kind, hybrid image+text document search system for biocuration. The system, MouseScholar, leverages an image modality taxonomy derived in collaboration with biocurators, in addition to figure segmentation, and classifiers components as a back-end and a streamlined front-end interface to search and present document results. We formally evaluated the system with ten biocurators on a mouse genome informatics biocuration dataset and collected feedback. The results demonstrate the benefits of blending text and image information when presenting scientific articles for biocuration.

Bioinformatics (2021), Volume 37(Suppl1), i468–i476, doi:10.1093/bioinformatics/btab331 The error is thus only in mis-typing the formulae themselves; not in the actual calculations of the precision and the recall used throughout the paper. As such, the other parts of the manuscript – specifically the experimental results reported, all stand as they appear in the original publication, and are not impacted by this correction.

MOTIVATION: Biomedical research findings are typically disseminated through publications. To simplify access to domain-specific knowledge while supporting the research community, several biomedical databases devote significant effort to manual curation of the literature-a labor intensive process. The first step toward biocuration requires identifying articles relevant to the specific area on which the database focuses. Thus, automatically identifying publications relevant to a specific topic within a large volume of publications is an important task toward expediting the biocuration process and, in turn, biomedical research. Current methods focus on textual contents, typically extracted from the title-and-abstract. Notably, images and captions are often used in publications to convey pivotal evidence about processes, experiments and results. RESULTS: We present a new document classification scheme, using both image and caption information, in addition to titles-and-abstracts. To use the image information, we introduce a new image representation, namely Figure-word, based on class labels of subfigures. We use word embeddings for representing captions and titles-and-abstracts. To utilize all three types of information, we introduce two information integration methods. The first combines Figure-words and textual features obtained from captions and titles-and-abstracts into a single larger vector for document representation; the second employs a meta-classification scheme. Our experiments and results demonstrate the usefulness of the newly proposed Figure-words for representing images. Moreover, the results showcase the value of Figure-words, captions and titles-and-abstracts in providing complementary information for document classification; these three sources of information when combined, lead to an overall improved classification performance. AVAILABILITY AND IMPLEMENTATION: Source code and the list of PMIDs of the publications in our datasets are available upon request.

The National Cancer Institute (NCI) is developing an integrated biomedical informatics infrastructure, the cancer Biomedical Informatics Grid (caBIG), to support collaboration within the cancer research community. A key part of the caBIG architecture is the establishment of terminology standards for representing data. In order to evaluate the suitability of existing controlled terminologies, the caBIG Vocabulary and Data Elements Workspace (VCDE WS) working group has developed a set of criteria that serve to assess a terminology's structure, content, documentation, and editorial process. This paper describes the evolution of these criteria and the results of their use in evaluating four standard terminologies: the Gene Ontology (GO), the NCI Thesaurus (NCIt), the Common Terminology for Adverse Events (known as CTCAE), and the laboratory portion of the Logical Objects, Identifiers, Names and Codes (LOINC). The resulting caBIG criteria are presented as a matrix that may be applicable to any terminology standardization effort.

The mouse genome informatics (MGI) resource, an in-depth resource for the genetics, genomics and biology of the laboratory mouse, provides free access to integrated data on diverse biological attributes, ranging from sequence to phenotype and disease model representation. MGI advances translational research through an integrated data platform that facilitates acquisition, explicit representation, and semantic querying and interpretation of multi-parametric genome-scale datasets, and fosters interoperability across different model organism systems and disparate data sources. To this end, MGI employs a set of logically rigorous, dynamic, and cross-referenceable ontologies to unambiguously describe current biological knowledge, expedite manual curation, and advance the informatics capacity to execute complex data mining tasks relevant to comparative and functional genomics. Major bio-ontologies developed and implemented at MGI include the gene ontology (GO), Mammalian Phenotype (MP) ontology, and adult mouse anatomical (MA) Dictionary, reviewed in this paper. All these share a common generic vocabulary infrastructure, and utilize identical annotation tools and web-based browsers to reinforce ontology-centric curation and support ontology-driven querying of the vocabularies and the associated knowledgebase.

Biology is a vast domain. The Mouse Genome Informatics (MGI) system, which focuses on the biology of the laboratory mouse, covers only a small, carefully chosen slice. Nevertheless, we deal with data of immense variety, deep complexity, and exponentially growing volume. Our role as an integration nexus is to add value by combining data sets of diverse types and origins, eliminating redundancy and resolving conflicts. We briefly describe some of the issues we face and approaches we have adopted to the integration problem.

Catherine A. Ball, Gavin Sherlock, Helen Parkinson, Philippe Rocca-Sera, Catherine Brooksbank, Helen C. Causton, Duccio Cavalieri, Terry Gaasterland, Pascal Hin

Data integration is key to knowledge discovery in the age of genomics and represents a major, long-standing challenge for the genome informatics community. Integration of data across heterogeneous genome databases requires the identification of common data entities and mechanisms to ensure referential integrity and persistence of these common entities even as our understanding of their biological properties changes. The Mouse Genome Informatics (MGI) database group employs multiple strategies for achieving data integration and maintaining inter-connectedness with other databases, including: (1) the use of permanent, unique accession IDs for identifying core data entities, (2) the application of nomenclature standards for naming genes and strains of mice, and (3) the development and implementation of controlled vocabularies and ontologies to ensure semantic consistency of biological concepts within and across model organism databases.