Prediction of Alzheimer's diagnosis using semi-supervised distance metric learning with label propagation

Computational Biology and Chemistry

Volumn 32, Issue 6, 2008, Pages 438-441

  Teramoto, Reiji ^a  

^a NEC CORPORATION   (Japan)

Author keywords

Alzheimer's disease; Diagnosis; Distance metric learning; Label propagation; Semi supervised learning

Indexed keywords

EDUCATION; LABELS; LEARNING SYSTEMS; MATHEMATICAL MODELS; SUPERVISED LEARNING;

ADABOOST; ALZHEIMER; ALZHEIMER'S DISEASE; ALZHEIMER'S DISEASES; BRAIN DAMAGES; CURRENT DIAGNOSTICS; DIAGNOSIS MODELS; DISTANCE METRIC LEARNING; DISTANCE METRIC LEARNINGS; EARLY STAGES; HIGH-PERFORMANCE; IRREVERSIBLE; LABEL PROPAGATION; LABEL PROPAGATIONS; LABELED DATUM; LEARNING METHODS; METRICS; RANDOM FORESTS; SEMI-SUPERVISED LEARNING; SUPERVISED LEARNING ALGORITHMS; SYNERGISTIC EFFECTS; TRAINING SAMPLES;

LEARNING ALGORITHMS;

ALZHEIMER DISEASE; ARTICLE; HUMAN; PREDICTION AND FORECASTING;

ALZHEIMER DISEASE; HUMANS; PREDICTIVE VALUE OF TESTS;

ALGORITHMS; DIAGNOSIS; DISEASES; EDUCATION; LABELS; MATHEMATICAL MODELS;

EID: 54149120229     PISSN: 14769271     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiolchem.2008.07.030     Document Type: Article

References (14)

1. Breiman L. Random Forests. Machine Learning 45 (2001) 5-32
2. Breiman, L., Cutler, A. (2003). Random Forests Manual v4.0. Technical report, UC Berkeley, Available from .
3. Breiman L., Friedman J., Olshen R., and Stone C. Classification and Regression Trees (1984), Chapman & Hall/CRC
4. Freund Y., and Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55 (1997) 119-139
5. German D.C., Gurnani P., Nandi A., Garner H.R., Fisher W., Diaz-Arrastia R., O'Suilleabhain P., and Rosenblatt K.P. Serum biomarkers for Alzheimer's disease: proteomic discovery. Biomed. Pharmacother. 61 (2007) 383-389
6. Hye A., Lynham S., Thambisetty M., Causevic M., Campbell J., Byers H.L., Hooper C., Rijsdijk F., Tabrizi S.J., Banner S., Shaw C.E., Foy C., Poppe M., Archer N., Hamilton G., Powell J., Brown R.G., Sham P., Ward.F M., and Lovestone S. Proteome-based plasma biomarkers for Alzheimer's disease. Brain 129 (2006) 3042-3050
7. McKhann G., Drachman D., Folstein M., Katzman R., Price D., and Stadlan E.M. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 34 (1984) 939-944
8. Ray S., Britschgi M., Herbert C., Takeda-Uchimura Y., Boxer A., Blennow K., Friedman L.F., Galasko D.R., Jutel M., Karydas A., Kaye J.A., Leszek J., Miller B.L., Minthon L., Quinn J.F., Rabinovici G.D., Robinson W.H., Sabbagh M.N., So Y.T., Sparks D.L., Tabaton M., Tinklenberg J., Yesavage J.A., Tibshirani R., and Wyss-Coray T. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat. Med. 13 (2007) 1359-1362
9. Vapnik V. Statistical Learning Theory (1998), Wiley, NY
10. Weinberger K., Blitzer J., and Saul L. Distance metric learning for large margin nearest neighbor classification. Adv. Neural Inf. Process. Syst. 18 (2006) 1473-1480
11. Xiao Z., Prieto D., Conrads T.P., Veenstra T.D., and Issaq H.J. Proteomic patterns: their potential for disease diagnosis. Mol. Cell. Endocrinol. 230 (2005) 95-106
12. Xing E.P., Ng A.Y., Jordan M.I., and Russell S. Distance metric learning, with application to clustering with side-information. Adv. Neural Inf. Process. Syst. 14 (2003) 505-514
13. Zhou D., Bousquet O., Lal N.T., Weston J., and Schökopf B. Learning with local and global consistency. Adv. Neural Inf. Process. Syst. 16 (2004) 321-328
14. Zhu X., Ghahramani Z., and Lafferty J. Semi-supervised learning using Gaussian fields and harmonic functions. Proceeding of the 20th International Conference on Machine Learning (ICML) (2003) 912-919