Text mining for bone biology

  • Authors:

  • Andrew Hoblitzell;Snehasis Mukhopadhyay;Qian You;Shiaofen Fang;Yuni Xia;Joseph Bidwell

  • Affiliations:

  • Indiana University -- Purdue University Indianapolis, Indianapolis, Indiana;Indiana University -- Purdue University Indianapolis, Indianapolis, Indiana;Indiana University -- Purdue University Indianapolis, Indianapolis, Indiana;Indiana University -- Purdue University Indianapolis, Indianapolis, Indiana;Indiana University -- Purdue University Indianapolis, Indianapolis, Indiana;Indiana University -- Purdue University Indianapolis, Indianapolis, Indiana

  • Venue:
  • Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
  • Year:
  • 2010

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Abstract

Osteoporosis, which is characterized by reduced bone mass and debilitating fractures, may reach epidemic proportions with the aging of the US population. The intensity of research in this field of study is reflected by the facts that The American Society of Bone and Mineral Research has a membership of nearly 4,000 physicians, clinical investigators, and basic research scientists from over fifty countries and that NIH is expected to spend over 200 million dollars on osteoporosis research alone in 2010. Bone biologists may be overwhelmed by the amount of literature constantly being generated, thus the identification and extraction of existing and novel relationships among biological entities or terms appearing in the biological literature is an ongoing problem. The problem has become more pressing with the development of large online publicly available databases of biological literature. Extraction and visualization of relationships between biological entities appearing in these databases offers the opportunity of keeping researchers up-to-date in their research domain. This may be achieved through helping them visualize possible biological pathways and by generating likely new hypotheses concerning novel interactions through methods such as transitive closure network flow. All generated predictions can be verified against already existing data, and possible new relationships can be verified against experiment. This paper presents a method for the extraction and visualization of potentially meaningful relationships.