Ontology-Based MEDLINE Document Classification

An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents

Cancer Biology Data Curation at the Mouse Tumor Biology Database (MTB)

Many advances in the field of cancer biology have been made using mouse models of human cancer. The Mouse Tumor Biology (MTB, "http://tumor.informatics.jax.org":http://tumor.informatics.jax.org) database provides web-based access to data on spontaneous and induced tumors from genetically defined mice (inbred, hybrid, mutant, and genetically engineered strains of mice). These data include standardized tumor names and classifications, pathology reports and images, mouse genetics, genomic and cytogenetic changes occurring in the tumor, strain names, tumor frequency and latency, and literature citations.

Although primary source for the data represented in MTB is peer-reviewed scientific literature an increasing amount of data is derived from disparate sources. MTB includes annotated histopathology images and cytogenetic assay images for mouse tumors where these data are available from The Jackson Laboratory’s mouse colonies and from outside contributors. MTB encourages direct submission of mouse tumor data and images from the cancer research community and provides investigators with a web-accessible tool for image submission and annotation. 

Integrated searches of the data in MTB are facilitated by the use of several controlled vocabularies and by adherence to standard nomenclature. MTB also provides links to other related online resources such as the Mouse Genome Database, Mouse Phenome Database, the Biology of the Mammary Gland Web Site, Festing's Listing of Inbred Strains of Mice, the JAX® Mice Web Site, and the Mouse Models of Human Cancers Consortium's Mouse Repository. 

MTB provides access to data on mouse models of cancer via the internet and has been designed to facilitate the selection of experimental models for cancer research, the evaluation of mouse genetic models of human cancer, the review of patterns of mutations in specific cancers, and the identification of genes that are commonly mutated across a spectrum of cancers.

MTB is supported by NCI grant CA089713

The Mouse Genome Database (MGD): mouse biology and model systems

The Mouse Genome Database, (MGD, http://www.informatics.jax.org/), integrates genetic, genomic and phenotypic information about the laboratory mouse, a primary animal model for studying human biology and disease. MGD data content includes comprehensive characterization of genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data within MGD are obtained from diverse sources including manual curation of the biomedical literature, direct contributions from individual investigator's laboratories and major informatics resource centers such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development of data and semantic standards such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. MGD provides a data-mining platform that enables the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the association of gene trap data with mouse genes and a new batch query capability for customized data access and retrieval

The mouse genome database (MGD): new features facilitating a model system

The mouse genome database (MGD, ), the international community database for mouse, provides access to extensive integrated data on the genetics, genomics and biology of the laboratory mouse. The mouse is an excellent and unique animal surrogate for studying normal development and disease processes in humans. Thus, MGD's primary goals are to facilitate the use of mouse models for studying human disease and enable the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Core MGD data content includes gene characterization and functions, phenotype and disease model descriptions, DNA and protein sequence data, polymorphisms, gene mapping data and genome coordinates, and comparative gene data focused on mammals. Data are integrated from diverse sources, ranging from major resource centers to individual investigator laboratories and the scientific literature, using a combination of automated processes and expert human curation. MGD collaborates with the bioinformatics community on the development of data and semantic standards, and it incorporates key ontologies into the MGD annotation system, including the Gene Ontology (GO), the Mammalian Phenotype Ontology, and the Anatomical Dictionary for Mouse Development and the Adult Anatomy. MGD is the authoritative source for mouse nomenclature for genes, alleles, and mouse strains, and for GO annotations to mouse genes. MGD provides a unique platform for data mining and hypothesis generation where one can express complex queries simultaneously addressing phenotypic effects, biochemical function and process, sub-cellular location, expression, sequence, polymorphism and mapping data. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the incorporation of single nucleotide polymorphism data and search tools, the addition of PIR gene superfamily classifications, phenotype data for NIH-acquired knockout mice, images for mouse phenotypic genotypes, new functional graph displays of GO annotations, and new orthology displays including sequence information and graphic displays

Quality control of injection molded eyewear by non-contact deflectometry

Occupational eye wear such as safety spectacles are manufactured by injection molding techniques. Testing of the assembled safety spectacle lenses in transmission is state of the art, but there is a lack of surface measurement systems for occupational safety lenses. The purpose of this work was to validate a deflectometric setup for topography measurement, detection of defects and visualization of the polishing quality, e.g. casting indentations or impressions, for the production process of safety spectacles. The setup is based on a customized stereo phase measuring deflectometer (PMD), equipped with 3 cameras with f’1,2 = 16 mm and f’3 = 8.5 mm and a specified measurement uncertainty of ± 3 μm. Sixteen plastic lenses and 8 corresponding injection molds from 4 parallel cavities were used for validation of the deflectometer. For comparison an interferometric method and a reference standard (< λ/10 super polished) was used. The accuracy and bias with a spherical safety spectacle sample was below 1 μm, according to DIN ISO 5725-2.2002-12. The repeatability was 2.1 μm and 35.7 μm for a blind radius fit. In conclusion, the PMD technique is an appropriate tool for characterizing occupational safety spectacle and injections mold surfaces. With the presented setup we were able to quantify the surface quality. This can be useful and may optimize the quality of the end product, in addition to standardized measuring systems in transmission

Disease Ontology: improving and unifying disease annotations across species.

Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD\u27s and RGD\u27s disease term annotations identified new terms that enhance DO\u27s representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO\u27s domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO\u27s usability across human data, MGD, RGD and the rest of the model organism database community. Dis Model Mech 2018 Mar 12;11(3):dmm032839

The Mouse Genome Database: enhancements and updates

The Mouse Genome Database (MGD) is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) database resource and serves as the primary community model organism database for the laboratory mouse. MGD is the authoritative source for mouse gene, allele and strain nomenclature and for phenotype and functional annotations of mouse genes. MGD contains comprehensive data and information related to mouse genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data for MGD are obtained from diverse sources including manual curation of the biomedical literature and direct contributions from individual investigator’s laboratories and major informatics resource centers, such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology and the Mammalian Phenotype Ontology. Recent improvements in MGD described here includes integration of mouse gene trap allele and sequence data, integration of gene targeting information from the International Knockout Mouse Consortium, deployment of an MGI Biomart, and enhancements to our batch query capability for customized data access and retrieval

Disease model curation improvements at Mouse Genome Informatics

Optimal curation of human diseases requires an ontology or structured vocabulary that contains terms familiar to end users, is robust enough to support multiple levels of annotation granularity, is limited to disease terms and is stable enough to avoid extensive reannotation following updates. At Mouse Genome Informatics (MGI), we currently use disease terms from Online Mendelian Inheritance in Man (OMIM) to curate mouse models of human disease. While OMIM provides highly detailed disease records that are familiar to many in the medical community, it lacks structure to support multilevel annotation. To improve disease annotation at MGI, we evaluated the merged Medical Subject Headings (MeSH) and OMIM disease vocabulary created by the Comparative Toxicogenomics Database (CTD) project. Overlaying MeSH onto OMIM provides hierarchical access to broad disease terms, a feature missing from the OMIM. We created an extended version of the vocabulary to meet the genetic disease-specific curation needs at MGI. Here we describe our evaluation of the CTD application, the extensions made by MGI and discuss the strengths and weaknesses of this approach