Preprint Article Version 1 NOT YET PEER-REVIEWED

Bio-Resource Exchange: Study of Prevalence of Antibody Donation and Development of a Web Portal to Facilitate it

  1. Language Technologies Institute, Carnegie Mellon University
  2. Language Technologies Institute, Carnegie Mellon University and Department of Biomedical Informatics, and Intelligent Systems Program, University of Pittsburgh
Version 1 : Received: 5 October 2016 / Approved: 5 October 2016 / Online: 5 October 2016 (15:08:32 CEST)

How to cite: Subramanian, S.; Ganapathiraju, M. Bio-Resource Exchange: Study of Prevalence of Antibody Donation and Development of a Web Portal to Facilitate it. Preprints 2016, 2016100012 (doi: 10.20944/preprints201610.0012.v1). Subramanian, S.; Ganapathiraju, M. Bio-Resource Exchange: Study of Prevalence of Antibody Donation and Development of a Web Portal to Facilitate it. Preprints 2016, 2016100012 (doi: 10.20944/preprints201610.0012.v1).

Abstract

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: madhavi@pitt.edu, madhavi@cs.cmu.edu Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

Subject Areas

data exchange; resource donations; text mining

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