Send to

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2006 Apr 1;22(7):857-65. Epub 2006 Jan 12.

LSAT: learning about alternative transcripts in MEDLINE.

Author information

  • 1European Molecular Biology Laboratory, Heidelberg, Germany.



Generation of alternative transcripts from the same gene is an important biological event due to their contribution in creating functional diversity in eukaryotes. In this work, we choose the task of extracting information around this complex topic using a two-step procedure involving machine learning and information extraction.


In the first step, we trained a classifier that inductively learns to identify sentences about physiological transcript diversity from the MEDLINE abstracts. Using a large hand-built corpus, we compared the sentence classification performance of various text categorization methods. Support vector machines (SVMs) followed by the maximum entropy classifier outperformed other methods for the sentence classification task. The SVM with the radial basis function kernel and optimized parameters achieved Fbeta-measure of 91% during the 4-fold cross validation and of 74% when applied to all sentences in more than 12 million abstracts of MEDLINE. In the second step, we identified eight frequently present semantic categories in the sentences and performed a limited amount of semantic role labeling. The role labeling step also achieved very high Fbeta-measure for all eight categories.


The results of our two-step procedure are summarized in the LSAT database of alternative transcripts. LSAT is available at CONTACT:


Supplementary data are available at Bioinformatics online.

[PubMed - indexed for MEDLINE]
Free full text
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire
    Loading ...
    Support Center