title: Connecting the dots between PubMed abstracts.
abstract: BACKGROUND
  There are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and diseases. Each article investigates subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must integrate information from multiple publications. Particularly, unraveling relationships between extra-cellular inputs and downstream molecular response mechanisms requires integrating conclusions from diverse publications.
  
  
  METHODS
  We present an automated approach to biological knowledge discovery from PubMed abstracts, suitable for "connecting the dots" across the literature. We describe a storytelling algorithm that, given a start and end publication, typically with little or no overlap in content, identifies a chain of intermediate publications from one to the other, such that neighboring publications have significant content similarity. The quality of discovered stories is measured using local criteria such as the size of supporting neighborhoods for each link and the strength of individual links connecting publications, as well as global metrics of dispersion. To ensure that the story stays coherent as it meanders from one publication to another, we demonstrate the design of novel coherence and overlap filters for use as post-processing steps.
  
  
  CONCLUSIONS
  WE DEMONSTRATE THE APPLICATION OF OUR STORYTELLING ALGORITHM TO THREE CASE STUDIES: i) a many-one study exploring relationships between multiple cellular inputs and a molecule responsible for cell-fate decisions, ii) a many-many study exploring the relationships between multiple cytokines and multiple downstream transcription factors, and iii) a one-to-one study to showcase the ability to recover a cancer related association, viz. the Warburg effect, from past literature. The storytelling pipeline helps narrow down a scientist's focus from several hundreds of thousands of relevant documents to only around a hundred stories. We argue that our approach can serve as a valuable discovery aid for hypothesis generation and connection exploration in large unstructured biological knowledge bases.
authors.affiliation: Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America. msh@vt.edu
authors.forename: M Shahriar
authors.initials: MS
authors.lastname: Hossain
authors.forename: Joseph
authors.initials: J
authors.lastname: Gresock
authors.forename: Yvette
authors.initials: Y
authors.lastname: Edmonds
authors.forename: Richard
authors.initials: R
authors.lastname: Helm
authors.forename: Malcolm
authors.initials: M
authors.lastname: Potts
authors.forename: Naren
authors.initials: N
authors.lastname: Ramakrishnan
mesh_terms: D000375:Aging; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology; D016207:Cytokines / Q000378:metabolism; D057225:Data Mining / Q000379:methods*; D006019:Glycolysis; D006801:Humans; D009369:Neoplasms / Q000378:metabolism / Q000473:pathology; D039781:PubMed; D011642:Publications; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D016207:Cytokines; D014157:Transcription Factors