Biological expression language (BEL) is one of the main formal representation 
models of biological networks. The primary source of information for curating 
biological networks in BEL representation has been literature. It remains a 
challenge to identify relevant articles and the corresponding evidence 
statements for curating and validating BEL statements. In this paper, we 
describe BELTracker, a tool used to retrieve and rank evidence sentences from 
PubMed abstracts and full-text articles for a given BEL statement (per the 2015 
task requirements of BioCreative V BEL Task). The system is comprised of three 
main components, (i) translation of a given BEL statement to an information 
retrieval (IR) query, (ii) retrieval of relevant PubMed citations and (iii) 
finding and ranking the evidence sentences in those citations. BELTracker uses a 
combination of multiple approaches based on traditional IR, machine learning, 
and heuristics to accomplish the task. The system identified and ranked at least 
one fully relevant evidence sentence in the top 10 retrieved sentences for 72 
out of 97 BEL statements in the test set. BELTracker achieved a precision of 
0.392, 0.532 and 0.615 when evaluated with three criteria, namely full, relaxed 
and context criteria, respectively, by the task organizers. Our team at Mayo 
Clinic was the only participant in this task. BELTracker is available as a 
RESTful API and is available for public use.Database URL: 
http://www.openbionlp.org:8080/BelTracker/finder/Given_BEL_Statement.