Multilevel text mining for bone biology

Abstract

Osteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results.

The potential relationship discovery model based on result fusion for biomedical medicine research

“…Weeber et al [19] tries to discover new relationships between medicines and diseases in biological literature through a DAD system. Some researchers have set a specific context to infer relationships between biological objects [20, 21]. Lee [6] propose a new usage of dietary and pharmacological substances according to the ABC model.…”

Emerging Computational Methods for the Life Sciences Workshop Special Issue

“…This collection of papers was selected from those presented at the Emerging Computational Methods for the Life Sciences Workshop of the ACM HPDC 2010 conference , Chicago, Illinois June 22, 2010. The papers were all enhanced over the conference versions and separately reviewed.…”