Improved ENSPART for DNA Motif Prediction

Choong, Allen Chieng Hoon and Lee, Nung Kion and Bong, Chih How and Norshafarina, Omar (2017) Improved ENSPART for DNA Motif Prediction. International Journal of Business and Society, 18 (S4). pp. 1-6. ISSN 15116670

SCT-073-revised-deposit (abstrak).pdf

Download (240kB) | Preview
Official URL:


In our previous work we proposed ENSPART-an ensemble method for DNA motif discovery which partitions input dataset into several equal size subsets runs by several distinct tools for candidate motif prediction. The candidate motifs obtained from different data subsets are merged to obtain the final motifs. Nevertheless, the original ENSPART has several limitations: (1) the same background sequences are used for the calculation of Receiver Operating Cost (ROC) of motifs obtained from different datasets. This causes bias because different datasets might have different background distribution; (2) it does not consider the duplication of a motif and its reverse complement. This causes many redundant motifs in the result set which requires filtering. In this work, we extended the original ENSPART to solve those two issues. For the first issue, we employed background sequences that is based on the distribution of bases in the input sequences. As for the second issue, we employ a "triple" merging strategy to reduce redundant motifs. Our evaluation results indicate that the two improvements obtain better AUC values in comparison to the original implementation.

Item Type: Article
Uncontrolled Keywords: ENSPART, DNA, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Lee
Date Deposited: 03 Jan 2018 06:06
Last Modified: 03 Jan 2018 06:06

Actions (For repository members only: login required)

View Item View Item