TY - JOUR
T1 - A consensus method for prioritising drug-associated target proteins
AU - Shu, Gang
AU - Huang, Xiaodi
AU - Zhu, Shanfeng
N1 - Imported on 12 Apr 2017 - DigiTool details were: month (773h) = July, 2012; Journal title (773t) = International Journal of Data Mining and Bioinformatics. ISSNs: 1748-5673;
PY - 2012/7/1
Y1 - 2012/7/1
N2 - It is generally believed that the degree of a relation between two entities is likely to be stronger if they co-occur more often in the literature. Based on this assumption, several methods are used in biomedical text mining such as support, confidence, chi-square, odds ratio, lift, all-confidence, coherence, and pof. Comparing these eight methods, our work aims to find the best one. Also, we present a consensus approach that can further improve the performance. Experimental results on prioritising drug targets have shown that pof, coherence, and all-confidence in sequence are the top three. By integrating coherence into pof, the consensus method is the best one among all compared methods.
AB - It is generally believed that the degree of a relation between two entities is likely to be stronger if they co-occur more often in the literature. Based on this assumption, several methods are used in biomedical text mining such as support, confidence, chi-square, odds ratio, lift, all-confidence, coherence, and pof. Comparing these eight methods, our work aims to find the best one. Also, we present a consensus approach that can further improve the performance. Experimental results on prioritising drug targets have shown that pof, coherence, and all-confidence in sequence are the top three. By integrating coherence into pof, the consensus method is the best one among all compared methods.
KW - Biomedical text mining
KW - Consensus method
KW - Data mining
KW - Drug target prioritisation
UR - http://www.scopus.com/inward/record.url?scp=84864561216&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864561216&partnerID=8YFLogxK
U2 - 10.1504/IJDMB.2012.048197
DO - 10.1504/IJDMB.2012.048197
M3 - Article
C2 - 22724297
SN - 1748-5673
VL - 6
SP - 178
EP - 195
JO - International Journal of Data Mining and Bioinformatics
JF - International Journal of Data Mining and Bioinformatics
IS - 2
ER -