The devices, experimental scaffolds, and biomaterials ontology (DEB): a tool for mapping, annotation, and analysis of biomaterials' data

The size and complexity of the biomaterials literature makes systematic data analysis an excruciating manual task. A practical solution is creating databases and information resources. Implant design and biomaterials research can greatly benefit from an open database for systematic data retrieval. Ontologies are pivotal to knowledge base creation, serving to represent and organize domain knowledge. To name but two examples, GO, the gene ontology, and CheBI, Chemical Entities of Biological Interest ontology and their associated databases are central resources to their respective research communities. The creation of the devices, experimental scaffolds, and biomaterials ontology (DEB), an open resource for organizing information about biomaterials, their design, manufacture, and biological testing, is described. It is developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered are validated by members of the biomaterials research community. The ontology may be used for searching terms, performing annotations for machine learning applications, standardized meta-data indexing, and other cross-disciplinary data exploitation. The input of the biomaterials community to this effort to create data-driven open-access research tools is encouraged and welcomed.Preprin

NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table

Automatically linking MEDLINE abstracts to the Gene Ontology

Much has been written recently about the need for effective tools and methods for mining the wealth of information present in biomedical literature (Mack and Hehenberger, 2002; Blagosklonny and Pardee, 2001; Rindflesch et al., 2002)—the activity of conceptual biology. Keyword search engines operating over large electronic document stores (such as PubMed and the PNAS) offer some help, but there are fundamental obstacles that limit their effectiveness. In the first instance, there is no general consensus among scientists about the vernacular to be used when describing research about genes, proteins, drugs, diseases, tissues and therapies, making it very difficult to formulate a search query that retrieves the right documents. Secondly, finding relevant articles is just one aspect of the investigative process. A more fundamental goal is to establish links and relationships between facts existing in published literature in order to “validate current hypotheses or to generate new ones” (Barnes and Robertson, 2002)—something keyword search engines do little to support

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

Literature-based discovery of diabetes- and ROS-related targets

Abstract Background Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy.http://deepblue.lib.umich.edu/bitstream/2027.42/78315/1/1755-8794-3-49.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/2/1755-8794-3-49-S7.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/3/1755-8794-3-49-S10.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/4/1755-8794-3-49-S8.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/5/1755-8794-3-49-S3.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/6/1755-8794-3-49-S1.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/7/1755-8794-3-49-S4.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/8/1755-8794-3-49-S2.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/9/1755-8794-3-49-S12.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/10/1755-8794-3-49-S11.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/11/1755-8794-3-49-S9.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/12/1755-8794-3-49-S5.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/13/1755-8794-3-49-S6.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/14/1755-8794-3-49.pdfPeer Reviewe