Abstracts selection

Short documentation

The query topic (the training set) is defined by:

• the following PubMed query (NEW!)
• all the following MeSH terms (tree top)
• the following list of pmids

one per line

The training set is a set of scientific articles related to the same topic. You can write a PubMed query to build automatically a training set. Alternatively, you can use biomedical terms from the Medical Subject Headings (MeSH). Please input only exact terms. These terms can be found in the MeSH Browser or hierarchical tree. The procedure is detailed in the tutorial section. The training set can be also defined with your own list of articles (identified by PubMed identifiers, e.g. pmids). You can get a list of pmids in few clicks from the PubMed interface: go to the Pubmed page, make a query, select 'UI List' in the Display menu, and Send to file. Please see the related tutorial.

For demo: click on one of the following training set, and then on the 'Rank it' button:

• Mitochondria
• Host-Pathogen Interactions
• Alternative Splicing
• Cardiovascular Diseases and Smoking
• Phosphorylation and Protein Binding
• Phosphorylation-dependent molecular processes
• Neoplasms and Stem Cells
• Up-Regulation of physiological processes in Alzheimer Disease
• Computational Biology

The reference (the background set) is defined by:

• the whole Medline database
• the following list of pmids

one per line

The background set should use the whole medline database or a random selection of articles. You can also provide your own list to take into account a bias in the test set.

The abstracts to be ranked (the test set) are defined by:

• the training set
• the background set
• 10 000 randomly chosen recent abstracts
• publications of the last 1 2 3 4 5 6 7 8 9 10 11 month(s)
• the 1 2 3 4 5 6 7 8 9 10 -year(s) old abstracts
• the following list of pmids

one per line

The test set defines abstracts which will be ranked by the MedlineRanker program. Ranking all the medline abstracts of the last months or years may be long. The processing speed is approximately 1 Million abstracts (~2 years old abstracts) per minute after initialization steps. The speed may vary depending on the server load.

Please cite:

• MedlineRanker: flexible ranking of biomedical literature.
  Fontaine JF, Barbosa-Silva A, Schaefer M, Huska MR, Muro EM, Andrade-Navarro MA.
  Nucleic Acids Research 2009 May 8; doi: 10.1093/nar/gkp353

Please send me your comments and suggestions: JF's email