ArticleTechnological forecasting and social change2011
Literature-related discovery: Potential treatments and preventatives for SARS.
Article in Technological forecasting and social change, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 39 citations in OpenAlex.
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- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Human coronaviruses and therapeutic drug discovery.Infectious diseases of poverty · 2021Review
- Impact of COVID-19 on the travel and tourism industry.Technological forecasting and social change · 2021Article
- Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest.Frontiers in research metrics and analytics · 2021Article
- Using data mining techniques to fight and control epidemics: A scoping review.Health and technology · 2021Article
- Potential use of polyphenols in the battle against COVID-19.Current opinion in food science · 2020Review
- A Bibliometric Analysis of Corona Pandemic in Social Sciences: A Review of Influential Aspects and Conceptual Structure.IEEE access : practical innovations, open solutions · 2020Article
- Tracking and Mining the COVID-19 Research Literature.Frontiers in research metrics and analytics · 2020Article
- A systematic review on literature-based discovery workflow.PeerJ. Computer science · 2019Article
- Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation.Technological forecasting and social change · 2017Article
- Synergistic effect of methionine encephalin (MENK) combined with pidotimod(PTD) on the maturation of murine dendritic cells (DCs).Human vaccines & immunotherapeutics · 2013Article
- Literature-related discovery and innovation - update.Technological forecasting and social change · 2012Article
- Can the vector space model be used to identify biological entity activities?BMC genomics · 2011Article
- Literature-related discovery: Potential treatments and preventatives for SARS.Technological forecasting and social change · 2011Article
- Quantitative mapping of scientific research-The case of electrical conducting polymer nanocomposite.Technological forecasting and social change · 2011Article
- What the papers say: text mining for genomics and systems biology.Human genomics · 2010Review
- Quantitative mapping of patented technology - The case of electrical conducting polymer nanocomposite.Technological forecasting and social change · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Literature-related discovery (LRD) is the linking of two or more previously disjoint concepts in order to produce novel, interesting, plausible, and intelligible connections (i.e., potential discovery). LRD has been used to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. Severe acute respiratory syndrome (SARS) was the first pandemic of the 21st century. SARS was eventually controlled through increased hygienic measures (e.g., face mask protection, frequent hand washing, living quarter disinfection), travel restrictions, and quarantine. According to recent reviews of SARS, none of the drugs that were used during the pandemic worked. For the present paper, SARS was selected as the first application of LRD to an infectious disease. The main goal of this research was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop/reverse the progression of SARS. The MeSH taxonomy of Medline was used to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. To enhance the volume of potential discovery, databases were used in addition to Medline. These included the Science Citation Index (SCI) and, in contrast to previous work, a full text database. Because of the richness of the full text, 'surgical' queries were developed that targeted the exact types of potential discovery of interest while eliminating clutter more efficiently.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.