Discovering Relationship Associations in Life Sciences using Ontology and Inference

Over one million papers are published annually in life sciences. Bioinformatics and knowledge discovery fields aim to help researchers conduct scientific discovery using the existing published knowledge. Existing literature-based discovery methods and tools mainly use text-mining techniques to extract non-specified relationships between two concepts. We present an approach that uses semantic web techniques to measure the relevance between two relationships with specified types that involve a particular entity. We consider two highly relevant relationships as a relationship association. Relationship associations could help researchers generate scientific hypotheses or create computer-interpretable semantic descriptors for their papers. The relationship association extraction process is described and the results of experiments for extracting relationship associations from 392 semantic graphs representing MEDLINE papers are presented.

[1]  Chikako Shingyoji,et al.  Effects of Imposed Bending on Microtubule Sliding in Sperm Flagella , 2004, Current Biology.

[2]  D. Swanson.  Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[3]  Stephen A. Racunas,et al.  HyBrow: a prototype system for computer-aided hypothesis evaluation , 2004, ISMB/ECCB.

[4]  Michael D. Gordon,et al.  Literature-based discovery by lexical statistics , 1999 .

[5]  D. Swanson.  Migraine and Magnesium: Eleven Neglected Connections , 2015, Perspectives in biology and medicine.

[6]  R. Kamiya,et al.  A tektin homologue is decreased in chlamydomonas mutants lacking an axonemal inner-arm dynein. , 2004, Molecular biology of the cell.

[7]  Pradeep Ravikumar,et al.  A Comparison of String Distance Metrics for Name-Matching Tasks , 2003, IIWeb.

[8]  Chikako Shingyoji,et al.  Central-pair-linked regulation of microtubule sliding by calcium in flagellar axonemes , 2003, Journal of Cell Science.

[9]  Marc Weeber,et al.  Case Report: Generating Hypotheses by Discovering Implicit Associations in the Literature: A Case Report of a Search for New Potential Therapeutic Uses for Thalidomide , 2003, J. Am. Medical Informatics Assoc..

[10]  Pat Langley.  The computational support of scientific discovery , 2000, Int. J. Hum. Comput. Stud..

[11]  Takenori Yamada,et al.  Diameter oscillation of axonemes in sea-urchin sperm flagella. , 2004, Biophysical journal.

[12]  Gregor Novak,et al.  Just-in-Time Teaching in biology: creating an active learner classroom using the Internet. , 2004, Cell biology education.

[13]  Joyce A. Mitchell,et al.  Using literature-based discovery to identify disease candidate genes , 2005, Int. J. Medical Informatics.

[14]  Neil R. Smalheiser,et al.  Artificial Intelligence An interactive system for finding complementary literatures : a stimulus to scientific discovery , 1995 .

[15]  D. Swanson.  Somatomedin C and Arginine: Implicit Connections between Mutually Isolated Literatures , 2015, Perspectives in biology and medicine.

[16]  Steven B. Kraines,et al.  Explicit scientific knowledge comparison based on semantic description matching , 2008, ASIST.

[17]  S. Aoyama,et al.  Cyclical interactions between two outer doublet microtubules in split flagellar axonemes. , 2005, Biophysical journal.

[18]  Steven B. Kraines,et al.  EKOSS: A Knowledge-User Centered Approach to Knowledge Sharing, Discovery, and Integration on the Semantic Web , 2006, SEMWEB.

[19]  Barend Mons,et al.  Online tools to support literature-based discovery in the life sciences , 2005, Briefings Bioinform..

[20]  Saso Dzeroski,et al.  Supporting Discovery in Medicine by Association Rule Mining in Medline and UMLS , 2001, MedInfo.

[21]  Neil R. Smalheiser,et al.  Information discovery from complementary literatures: Categorizing viruses as potential weapons , 2001, J. Assoc. Inf. Sci. Technol..

[22]  Michael D. Gordon,et al.  Toward Discovery Support Systems: A Replication, Re-Examination, and Extension of Swanson's Work on Literature-Based Discovery of a Connection between Raynaud's and Fish Oil , 1996, J. Am. Soc. Inf. Sci..

[23]  Padmini Srinivasan,et al.  Text mining: Generating hypotheses from MEDLINE , 2004, J. Assoc. Inf. Sci. Technol..

[24]  Michael D. Gordon,et al.  Literature-Based Discovery by Lexical Statistics , 1999, J. Am. Soc. Inf. Sci..