Machine learning approaches for the neuroimaging study of Alzheimer's disease

Jieping Ye; Teresa Wu; Jing Li; Kewei Chen

doi:10.1109/MC.2011.117

Machine learning approaches for the neuroimaging study of Alzheimer's disease

Jieping Ye, Teresa Wu, Jing Li, Kewei Chen

Research output: Contribution to journal › Article › peer-review

41 Scopus citations

Abstract

Machine learning tools aid many Alzheimer's disease-related investigations by enabling multisource data fusion and biomarker identification as well as analysis of functional brain connectivity.

Original language	English (US)
Article number	5742017
Pages (from-to)	99-101
Number of pages	3
Journal	Computer
Volume	44
Issue number	4
DOIs	https://doi.org/10.1109/MC.2011.117
State	Published - Apr 2011

Keywords

Alzheimer's disease
Machine learning techniques
Multiple kernel learning
Neuroimaging

ASJC Scopus subject areas

Computer Science(all)

Access to Document

10.1109/MC.2011.117

Cite this

@article{de2541f3d53348d58fc1d4fd8cd59bae,

title = "Machine learning approaches for the neuroimaging study of Alzheimer's disease",

abstract = "Machine learning tools aid many Alzheimer's disease-related investigations by enabling multisource data fusion and biomarker identification as well as analysis of functional brain connectivity.",

keywords = "Alzheimer's disease, Machine learning techniques, Multiple kernel learning, Neuroimaging",

author = "Jieping Ye and Teresa Wu and Jing Li and Kewei Chen",

year = "2011",

month = apr,

doi = "10.1109/MC.2011.117",

language = "English (US)",

volume = "44",

pages = "99--101",

journal = "ACM SIGPLAN/SIGSOFT Workshop on Program Analysis for Software Tools and Engineering",

issn = "0018-9162",

publisher = "IEEE Computer Society",

number = "4",

}

TY - JOUR

T1 - Machine learning approaches for the neuroimaging study of Alzheimer's disease

AU - Ye, Jieping

AU - Wu, Teresa

AU - Li, Jing

AU - Chen, Kewei

PY - 2011/4

Y1 - 2011/4

N2 - Machine learning tools aid many Alzheimer's disease-related investigations by enabling multisource data fusion and biomarker identification as well as analysis of functional brain connectivity.

AB - Machine learning tools aid many Alzheimer's disease-related investigations by enabling multisource data fusion and biomarker identification as well as analysis of functional brain connectivity.

KW - Alzheimer's disease

KW - Machine learning techniques

KW - Multiple kernel learning

KW - Neuroimaging

UR - http://www.scopus.com/inward/record.url?scp=79953894950&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953894950&partnerID=8YFLogxK

U2 - 10.1109/MC.2011.117

DO - 10.1109/MC.2011.117

M3 - Article

AN - SCOPUS:79953894950

VL - 44

SP - 99

EP - 101

JO - ACM SIGPLAN/SIGSOFT Workshop on Program Analysis for Software Tools and Engineering

JF - ACM SIGPLAN/SIGSOFT Workshop on Program Analysis for Software Tools and Engineering

SN - 0018-9162

IS - 4

M1 - 5742017

ER -

Machine learning approaches for the neuroimaging study of Alzheimer's disease

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this