Protein Word Detection using Text Segmentation Techniques

Devi Ganesan, Ashish V. Tendulkar, Sutanu Chakraborti


Abstract
Literature in Molecular Biology is abundant with linguistic metaphors. There have been works in the past that attempt to draw parallels between linguistics and biology, driven by the fundamental premise that proteins have a language of their own. Since word detection is crucial to the decipherment of any unknown language, we attempt to establish a problem mapping from natural language text to protein sequences at the level of words. Towards this end, we explore the use of an unsupervised text segmentation algorithm to the task of extracting “biological words” from protein sequences. In particular, we demonstrate the effectiveness of using domain knowledge to complement data driven approaches in the text segmentation task, as well as in its biological counterpart. We also propose a novel extrinsic evaluation measure for protein words through protein family classification.
Anthology ID:
W17-2330
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
238–246
Language:
URL:
https://www.aclweb.org/anthology/W17-2330
DOI:
10.18653/v1/W17-2330
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2330.pdf