Title page for ETD etd-05092007-165611


Type of Document Master's Thesis
Author Gresock, Joseph Aaron
Author's Email Address jgresock@vt.edu
URN etd-05092007-165611
Title Finding Combinatorial Connections between Concepts in the Biomedical Literature
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Ramakrishnan, Naren Committee Chair
Helm, Richard Frederick Committee Member
Murali, T. M. Committee Member
Keywords
  • literature mining
  • Storytelling
  • stories
Date of Defense 2007-05-08
Availability unrestricted
Abstract
There are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and entire processes. Each article investigates particular subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must computationally integrate information across multiple publications. This is especially important in problems such as modeling cross-talk in signaling networks, designing drug therapies for combinatorial selectivity, and unraveling the role of gene interactions in deleterious phenotypes, where the cost of performing combinatorial screens is exorbitant. In this thesis, we present an automated approach to biological knowledge discovery from PubMed abstracts, suitable for unraveling combinatorial relationships. It involves the systematic application of a `storytelling' algorithm followed by a series of filtering and compression operations over the mined stories. Given a start and end publication, typically with little or no overlap in content, storytelling identifies a chain of intermediate publications from one to the other, such that neighboring publications have significant content similarity. Stories discovered thus provide an argued approach to relate distant concepts through compositions of related concepts. The chains of links employed by stories are then mined to find frequently reused sub-stories, which can be compressed to yield compact templates of connections. We demonstrate a successful application of storytelling to finding combinatorial connections between biological concepts using two application case studies.
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