Dataset Information

LitInspector: literature and signal transduction pathway mining in PubMed abstracts.

ABSTRACT: LitInspector is a literature search tool providing gene and signal transduction pathway mining within NCBI's PubMed database. The automatic gene recognition and color coding increases the readability of abstracts and significantly speeds up literature research. A main challenge in gene recognition is the resolution of homonyms and rejection of identical abbreviations used in a 'non-gene' context. LitInspector uses automatically generated and manually refined filtering lists for this purpose. The quality of the LitInspector results was assessed with a published dataset of 181 PubMed sentences. LitInspector achieved a precision of 96.8%, a recall of 86.6% and an F-measure of 91.4%. To further demonstrate the homonym resolution qualities, LitInspector was compared to three other literature search tools using some challenging examples. The homonym MIZ-1 (gene IDs 7709 and 9063) was correctly resolved in 87% of the abstracts by LitInspector, whereas the other tools achieved recognition rates between 35% and 67%. The LitInspector signal transduction pathway mining is based on a manually curated database of pathway names (e.g. wingless type), pathway components (e.g. WNT1, FZD1), and general pathway keywords (e.g. signaling cascade). The performance was checked for 10 randomly selected genes. Eighty-two per cent of the 38 predicted pathway associations were correct. LitInspector is freely available at http://www.litinspector.org/.

SUBMITTER: Frisch M

PROVIDER: S-EPMC2703902 | biostudies-other | 2009 Jul

REPOSITORIES: biostudies-other

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LitInspector: literature and signal transduction pathway mining in PubMed abstracts.

Frisch Matthias M Klocke Bernward B Haltmeier Manuela M Frech Kornelie K

Nucleic acids research 20090505 Web Server issue

LitInspector is a literature search tool providing gene and signal transduction pathway mining within NCBI's PubMed database. The automatic gene recognition and color coding increases the readability of abstracts and significantly speeds up literature research. A main challenge in gene recognition is the resolution of homonyms and rejection of identical abbreviations used in a 'non-gene' context. LitInspector uses automatically generated and manually refined filtering lists for this purpose. The ...[more]

PMID: 19417065

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Project description:BackgroundThe emerging field of epidemiological criminology studies the intersection between public health and justice systems. To increase the value of and reduce waste in research activities in this area, it is important to perform transparent research priority setting considering the needs of research beneficiaries and end users along with a systematic assessment of the existing research activities to address gaps and harness opportunities.ObjectiveIn this study, we aimed to examine published research outputs in epidemiological criminology to assess gaps between published outputs and current research priorities identified by prison stakeholders.MethodsA rule-based method was applied to 23,904 PubMed epidemiological criminology abstracts to extract the study determinants and outcomes (ie, "themes"). These were mapped against the research priorities identified by Australian prison stakeholders to assess the differences from research outputs. The income level of the affiliation country of the first authors was also identified to compare the ranking of research priorities in countries categorized by income levels.ResultsOn an evaluation set of 100 abstracts, the identification of themes returned an F1-score of 90%, indicating reliable performance. More than 53.3% (11,927/22,361) of the articles had at least 1 extracted theme; the most common was substance use (1533/11,814, 12.97%), followed by HIV (1493/11,814, 12.64%). The infectious disease category (2949/11,814, 24.96%) was the most common research priority category, followed by mental health (2840/11,814, 24.04%) and alcohol and other drug use (2433/11,814, 20.59%). A comparison between the extracted themes and the stakeholder priorities showed an alignment for mental health, infectious diseases, and alcohol and other drug use. Although behavior- and juvenile-related themes were common, they did not feature as prison priorities. Most studies were conducted in high-income countries (10,083/11,814, 85.35%), while countries with the lowest income status focused half of their research on infectious diseases (47/91, 52%).ConclusionsThe identification of research themes from PubMed epidemiological criminology research abstracts is possible through the application of a rule-based text mining method. The frequency of the investigated themes may reflect historical developments concerning disease prevalence, treatment advances, and the social understanding of illness and incarcerated populations. The differences between income status groups are likely to be explained by local health priorities and immediate health risks. Notable gaps between stakeholder research priorities and research outputs concerned themes that were more focused on social factors and systems and may reflect publication bias or self-publication selection, highlighting the need for further research on prison health services and the social determinants of health. Different jurisdictions, countries, and regions should undertake similar systematic and transparent research priority-setting processes.

Project description:BackgroundThe Enteropathogen Resource Integration Center (ERIC; http://www.ericbrc.org) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process.DescriptionWe have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application.ConclusionOur Text Mining application is available online on the ERIC website (http://www.ericbrc.org/portal/eric/articles). The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.

Dataset Information

LitInspector: literature and signal transduction pathway mining in PubMed abstracts.

Publications

LitInspector: literature and signal transduction pathway mining in PubMed abstracts.

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