relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/113/
title: GASVM: A Genetic Algorithm for Improving Gene Regulatory Activity Prediction
creator: Duc, Dong Do
creator: Le, Tri-Thanh
creator: Vu, Trung-Nghia
creator: Dinh, H.Q.
creator: Hoang, Xuan-Huan
subject: Information Technology (IT)
description: Gene regulatory activity prediction problem is one of the important steps to understand the significant factors for gene regulation in biology. The advents of recent sequencing technologies allow us to deal with this task efficiently. Amongst these, Support Vector Machine (SVM) has been applied successfully up to more than 80 accuracy in the case of predicting gene regulatory activity in Drosophila embryonic development. In this paper, we introduce a metaheuristic based on genetic algorithm (GA) to select the best parameters for regulatory prediction from transcriptional factor binding profiles. Our approach helps to improve more than 10 accuracy compared to the traditional grid search. The improvements are also significantly supported by biological experimental data. Thus, the proposed method helps boosting not only the prediction performance but also the potentially biological insights.
date: 2012-03-01
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Duc, Dong Do and Le, Tri-Thanh and Vu, Trung-Nghia and Dinh, H.Q. and Hoang, Xuan-Huan  (2012) GASVM: A Genetic Algorithm for Improving Gene Regulatory Activity Prediction.  In: 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), February 27 - March 1, 2012, Ho Chi Minh city.     
relation: http://dx.doi.org/10.1109/rivf.2012.6169861