Paper Info
Title
NTTMUNSW BioC modules for recognizing and normalizing species and gene/protein mentions.
Abstract
In recent years, the number of published biomedical articles has increased as researchers have focused on biological domains to investigate the functions of biological objects, such as genes and proteins. However, the ambiguous nature of genes and their products have rendered the literature more complex for readers and curators of molecular interaction databases. To address this challenge, a normalization technique that can link variants of biological objects to a single, standardized form was applied. In this work, we developed a species normalization module, which recognizes species names and normalizes them to NCBI Taxonomy IDs. Unlike most previous work, which ignored the prefix of a gene name that represents an abbreviation of the species name to which the gene belongs, the recognition results of our module include the prefixed species. The developed species normalization module achieved an overall F-score of 0.954 on an instance-level species normalization corpus. For gene normalization, two separate modules were respectively employed to recognize gene mentions and normalize those mentions to their Entrez Gene IDs by utilizing a multistage normalization algorithm developed for processing full-text articles. All of the developed modules are BioC-compatible. NET framework libraries and are publicly available from the NuGet gallery.
Year
DOI
Venue
2016
10.1093/database/baw111
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Field
DocType
Volume
Data mining,Normalization (statistics),Gene,Biological objects,Information retrieval,.NET Framework,Computer science,Prefix,Species name,Entrez Gene,Bioinformatics,NCBI Taxonomy
Journal
2016
ISSN
Citations 
PageRank 
1758-0463
2
0.43
References 
Authors
11
4
Name
Order
Citations
PageRank
Hong-Jie Dai128821.58
Onkar Singh2142.09
Jitendra Jonnagaddala34610.28
Emily Chia-Yu Su4796.52