Project Director
Tanis, Craig
Department Examiner
Dumas, Joseph D.
Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Comparative studies have been powerful tools in generating a broad understanding about the evolution of animal social systems but they currently rely on the slow, manual process of reading thousands of abstracts and papers from research databases. A web application was created for researchers conducting a comparative survey, in order to speed up their research. This web application automates the retrieval of research papers and their selection process. Using previously obtained data sets on the orders Artiodacytla and Lagomorph, a machine learning application was created to classify the papers. These techniques and tools should greatly increase the speed at which researchers are able to compile papers and improve progress towards publication and scientific advancement.
Acknowledgments
Special Thanks to Craig Tanis, Loren Hayes, Ashley Carpenter, Thomas Wiegand, Hannah Margavio, Aaron Crawford, Mike Ward, Chris Dowell, Joseph Dumas and Andrew Nguyen. This project would also like to thank the Center of Excellence in Applied Computational Science and Engineering (CEACSE) for providing a grant for this project along with the UTC Department of Biology, Geology and Environmental Science for their cooperation.
Degree
B. S.; An honors thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Bachelor of Science.
Date
5-2020
Subject
Computational biology; Natural language processing (Computer science)
Discipline
Computational Biology | Software Engineering
Document Type
Theses
Extent
28 leaves.
DCMI Type
Text
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
Date Available
5-1-2020
Recommended Citation
Suggs, Evan, "Meta-analysis of biological research literature" (2020). Honors Theses.
https://scholar.utc.edu/honors-theses/274
Department
Dept. of Computer Science and Engineering