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ActiveMotif: Interactive motif discovery with human feedback

Authors
Kim, YounghoonLee, WoongheeKim, Keonwoo
Issue Date
Jul-2017
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp 2736 - 2739
Pages
4
Indexed
SCIE
SCOPUS
Journal Title
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Start Page
2736
End Page
2739
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/11591
DOI
10.1109/EMBC.2017.8037423
ISSN
1557-170X
Abstract
Motif detection, which is to discover short patterns involved in many important biological processes, has been recently raised as an important task in bioinformatics. The traditional algorithms to find a sequence motif have been developed using machine learning only without involving the experience and domain knowledge of human experts effectively. In this paper, we propose an interactive motif discovery system by introducing a new learning algorithm, by generalizing a well-known statistical motif model, whose inference can be shepherded by human feedback. © 2017 IEEE.
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Kim, Young hoon
ERICA 소프트웨어융합대학 (DEPARTMENT OF ARTIFICIAL INTELLIGENCE)
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