Comparative experiments on learning information extractors for proteins and their interactions.
OBJECTIVE
Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction efforts have been frustrated by the lack of conventions for describing human genes and proteins. We have developed and evaluated a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting information on interactions between the proteins.


METHODS
We used a variety of machine learning methods to automatically develop information extraction systems for extracting information on gene/protein name, function and interactions from Medline abstracts. We present cross-validated results on identifying human proteins and their interactions by training and testing on a set of approximately 1000 manually-annotated Medline abstracts that discuss human genes/proteins.


RESULTS
We demonstrate that machine learning approaches using support vector machines and maximum entropy are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions with higher precision than manually-developed rules.


CONCLUSIONS
Our results show that it is promising to use machine learning to automatically build systems for extracting information from biomedical text. The results also give a broad picture of the relative strengths of a wide variety of methods when tested on a reasonably large human-annotated corpus.
Bunescu
Razvan
R
Department of Computer Sciences, University of Texas, Austin, TX 78712, USA. razvan@cs.utexas.edu
Ge
Ruifang
R
Kate
Rohit J
RJ
Marcotte
Edward M
EM
Mooney
Raymond J
RJ
Ramani
Arun K
AK
Wong
Yuk Wah
YW
D000465:Algorithms; D001185:Artificial Intelligence; D005103:Expert Systems; D005796:Genes / Q000235:genetics / Q000502:physiology; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D011506:Proteins / Q000145:classification / Q000502:physiology; D009626:Terminology as Topic
D011506:Proteins