Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness

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Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1016/J.NICL.2014.08.023
P932PMC publication ID4215532
P698PubMed publication ID25379423
P5875ResearchGate publication ID265170304

P50authorHilkka SoininenQ38636823
Eric WestmanQ41538282
Dag AarslandQ41599688
Patrizia MecocciQ46713839
Astri J. LundervoldQ47703124
Magda N. TsolakiQ57073392
Gerard J. P. van WestenQ30086276
Simon LovestoneQ30582986
P2093author name stringA Simmons
A V Lebedev
Alzheimer's Disease Neuroimaging Initiative and the AddNeuroMed consortium
B Vellas
I Kłoszewska
M G Kramberger
P2860cites workEnsemble sparse classification of Alzheimer's diseaseQ35830734
Decision support systems for clinical radiological practice -- towards the next generationQ36324860
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On areas of transition between entorhinal allocortex and temporal isocortex in the human brain. Normal morphology and lamina-specific pathology in Alzheimer's diseaseQ48538921
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Structure-based identification of OATP1B1/3 inhibitorsQ34338211
Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairmentQ34642304
P407language of work or nameEnglishQ1860
P921main subjectAlzheimer's diseaseQ11081
random forestQ245748
P304page(s)115-125
P577publication date2014-08-28
P1433published inNeuroImage: ClinicalQ22907265
P1476titleRandom Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness
P478volume6

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