Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration

Machine learning and data mining: strategies for hypothesis generation

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Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review

(2012) Graziella Orrù et al.   NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS

Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

(2012) Daoqiang Zhang et al.   PLoS One

A mixed effects least squares support vector machine model for classification of longitudinal data

(2011) Jan Luts et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies

(2010) Michael C. Wu et al.   AMERICAN JOURNAL OF HUMAN GENETICS

L2-norm multiple kernel learning and its application to biomedical data fusion

(2010) Shi Yu et al.   BMC BIOINFORMATICS

Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease

(2009) Denise Harold et al.   NATURE GENETICS

Explicit connections between longitudinal data analysis and kernel machines

(2009) N.D. Pearce et al.   Electronic Journal of Statistics

Combining Early Markers Strongly Predicts Conversion from Mild Cognitive Impairment to Alzheimer's Disease

(2008) Davangere P. Devanand et al.   BIOLOGICAL PSYCHIATRY

Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease

(2007) Harald Hampel et al.   Alzheimers & Dementia

Detection of Huntington's disease decades before diagnosis: the Predict-HD study

(2007) J S Paulsen et al.   JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY