Reference : An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Pr...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/41512

Title :	An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction
Language :	English
Author, co-author :	Cecchini, Vania Filipa [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Nguyen, Thanh-Phuong [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Pfau, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	De Landtsheer, Sébastien [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Sauter, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Publication date :	2019
Edition :	1st ed.
Main document title :	An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction
Author, co-author :	Cecchini, Vania Filipa
Publisher :	DA NANG PUBLISHING HOUSE
Pages :	5
Peer reviewed :	Yes
Audience :	International
ISBN :	9781728130026
City :	Da Nang
Country :	Vietnam
Event name :	2019 11TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SYSTEMS ENGINEERING (KSE)
Event date :	from 24-10-2019 to 26-10-2019
Event organizer :	IEEE
Event place (city) :	Da Nang City
Event country :	Vietnam
Keywords :	[en] metabolic disease ; protein-protein interaction network ; miRNA-target interaction ; machine learning ; disease gene prediction ; imbalanced data
Permalink :	http://hdl.handle.net/10993/41512
Other URL :	https://ieeexplore.ieee.org/document/8919337

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access Cecchini_et_al_KSE_2019.pdf Publisher postprint 570.13 kB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map