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Tools included on the Advanced Search page help users to: search for terms in a specific field, combine searches and build large, complex search strings, see how each query was translated by PubMed, and compare number of results for different queries.
E-utilities are tools that provide access to data outside of the regular NCBI web search interface. This may be helpful for retrieving search results for use in another environment. If you are interested in large-scale data mining on PubMed data, you may download the data for free from our FTP server. Please see the terms and conditions for data users.
The learned ranking algorithm combines over 150 signals that are helpful for finding best matching results. Most of these signals are computed from the query-document term pairs (e.g., number of term matches between the query and the document) while others are either specific to a document (e.g., publication type; publication year) or query (e.g., query length). The new ranking model was built on relevance data extracted from the anonymous and aggregated PubMed search logs over an extended period of time.
The date the citation record was first created in PubMed. Create Date can be helpful when checking PubMed for citations added since the last time a query was run. Create Date is not included in All Fields retrieval; the [crdt] search tag is required.
MeSH Subheadings are used with MeSH terms to help describe more completely a particular aspect of a subject. For example, the drug therapy of asthma is displayed as asthma/drug therapy; see MeSH/Subheading Combinations in MeSH Terms [mh] below.
In the case of PubMed, cookies store information about your interactions that may be needed later to perform a function. To use these interactive features you need to enable cookies on your computer. Consult your browser's help for information on enabling cookies.
The global weight of a term is greater for the less frequent terms. This is reasonable because the presence of a term that occurred in most of the documents would really tell one very little about a document. On the other hand, a term that occurred in only 100 documents of one million would be very helpful in limiting the set of documents of interest. A word that occurred in only 10 documents is likely to be even more informative and will receive an even higher weight.
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