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Find video protocols related to scientific articles indexed in Pubmed.
DDMGD: the database of text-mined associations between genes methylated in diseases from different species.
Nucleic Acids Res.
PUBLISHED: 11-16-2014
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Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.
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Combining position weight matrices and document-term matrix for efficient extraction of associations of methylated genes and diseases from free text.
PLoS ONE
PUBLISHED: 01-01-2013
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In a number of diseases, certain genes are reported to be strongly methylated and thus can serve as diagnostic markers in many cases. Scientific literature in digital form is an important source of information about methylated genes implicated in particular diseases. The large volume of the electronic text makes it difficult and impractical to search for this information manually.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.