TY - JOUR
T1 - A semi-supervised, weighted pattern-learning approach for extraction of gene regulation relationships from scientific literature
AU - Tang, Yi Tsung
AU - Kao, Hung Yu
AU - Tsai, Shaw Jenq
AU - Wang, Hei Chia
PY - 2014
Y1 - 2014
N2 - Moreover, the large amount of textual knowledge in the existing biomedical literature is growing rapidly, and the creation of manual patterns from the available literature is becoming more difficult. There is an increasing demand to extract potential generic regulatory relationships from unlabelled data sets. In this paper, we describe a Semi-Supervised, Weighted Pattern Learning method (SSWPL) to extract such generic regulatory information from the literature. SSWPL can build new regulatory patterns according to predefined initial patterns from unlabelled data in the literature. These constructed regulatory patterns are then used to extract generic regulatory information from PubMed abstracts. The results presented herein demonstrate that our method can be utilised to effectively extract generic regulatory relationships from the literature by using learned, weighted patterns through semi-supervised pattern learning.
AB - Moreover, the large amount of textual knowledge in the existing biomedical literature is growing rapidly, and the creation of manual patterns from the available literature is becoming more difficult. There is an increasing demand to extract potential generic regulatory relationships from unlabelled data sets. In this paper, we describe a Semi-Supervised, Weighted Pattern Learning method (SSWPL) to extract such generic regulatory information from the literature. SSWPL can build new regulatory patterns according to predefined initial patterns from unlabelled data in the literature. These constructed regulatory patterns are then used to extract generic regulatory information from PubMed abstracts. The results presented herein demonstrate that our method can be utilised to effectively extract generic regulatory relationships from the literature by using learned, weighted patterns through semi-supervised pattern learning.
UR - http://www.scopus.com/inward/record.url?scp=84901940753&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901940753&partnerID=8YFLogxK
U2 - 10.1504/IJDMB.2014.062147
DO - 10.1504/IJDMB.2014.062147
M3 - Article
C2 - 25757247
AN - SCOPUS:84901940753
SN - 1748-5673
VL - 9
SP - 401
EP - 416
JO - International Journal of Data Mining and Bioinformatics
JF - International Journal of Data Mining and Bioinformatics
IS - 4
ER -