7 papers:
KDD-2013-XiangYFWTY #learning #multi #predict- Multi-source learning with block-wise missing data for Alzheimer’s disease prediction (SX, LY, WF, YW, PMT, JY), pp. 185–193.
- KDD-2011-HuangLYFCWR #effectiveness #modelling #network
- Brain effective connectivity modeling for alzheimer’s disease by sparse gaussian bayesian network (SH, JL, JY, AF, KC, TW, ER), pp. 931–939.
- KDD-2011-SimonLJV #comprehension #image #using
- Understanding atrophy trajectories in alzheimer’s disease using association rules on MRI images (GJS, PWL, CRJJ, PV), pp. 369–376.
ICPR-2010-SilveiraM #image #using- Boosting Alzheimer Disease Diagnosis Using PET Images (MS, JSM), pp. 2556–2559.
- KDD-2009-SunPLCWLRY #estimation #mining
- Mining brain region connectivity for alzheimer’s disease study via sparse inverse covariance estimation (LS, RP, JL, KC, TW, JL, ER, JY), pp. 1335–1344.
- KDD-2008-YeCWLZPBJLAR #data fusion #semistructured data
- Heterogeneous data fusion for alzheimer’s disease study (JY, KC, TW, JL, ZZ, RP, MB, RJ, HL, GEA, ER), pp. 1025–1033.
- ICPR-v3-2006-HuS #complexity
- Regularity and Complexity of Human Electroencephalogram Dynamics: Applications to Diagnosis of Alzheimers Disease (ZH, PS), pp. 245–248.
