Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning

Bioinformatics

Volumn 28, Issue 12, 2012, Pages

  Wang, Hua ^a   Nie, Feiping ^a   Huang, Heng ^a   Risacher, Shannon L ^b   Saykin, Andrew J ^b   Shen, Li ^b  

^a The University of Texas at Arlington   (United States)

^b INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER;

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL INTELLIGENCE; BRAIN; COGNITIVE DEFECT; COMPUTER PROGRAM; GENETICS; GENOTYPE; HUMAN; METABOLISM; NEUROIMAGING; PHENOTYPE; QUANTITATIVE TRAIT LOCUS; SUPPORT VECTOR MACHINE;

ALGORITHMS; ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; BRAIN; COGNITION DISORDERS; GENOTYPE; HUMANS; NEUROIMAGING; PHENOTYPE; QUANTITATIVE TRAIT LOCI; SOFTWARE; SUPPORT VECTOR MACHINES;

EID: 84863509119     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts228     Document Type: Article

References (46)

1. Abney, S. (2002) Bootstrapping. Annual Meeting of the Association for Computational Linguistics, pp. 360-367.
2. Argyriou, A. et al. (2007) Multi-task feature learning. In Advances in Neural Information Processing System (NIPS), The MIT Press, pp. 41-48.
3. Argyriou, A. et al. (2008) Convex multitask feature learning. Machine Learning, 73, 243-272.
4. Ashburner, J. and Friston, K. (2000) Voxel-based morphometry-the methods. Neuroimage, 11, 805-821.
5. Bach, F. et al. (2004) Multiple Kernel Learning, Conic Duality, and the SMOAlgorithm. In International Conference on Machine Learning (ICML), ACM, pp. 6.
6. Batmanghelich, N. et al. (2009) A general and unifying framework for feature construction, in image-based pattern classification. Inf Process Med Imaging, 21, 423-434.
7. Beck, A. and Teboulle., M. (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci., 2, 183-202.
8. Bickel, S. and Scheffer, T. (2004) Multi-view clustering. In IEEE International Conference on Data Mining (ICDM), pp. 36.
9. Brefeld, U. and Scheffer, T. (2004) Co-em support vector learning. In International Conference on Machine Learning (ICML), ACM, pp. 16.
10. Dhillon, I.S. et al. (2003) Information-theoretic co-clustering. In ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) International Conference on Knowledge Discovery and Data Mining, pp. 89-98.
11. Efron, B. et al. (2004) Least angle regression. Ann. Stat., 32, 407-499.
12. Fan, Y. et al. (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage, 39, 1731-1743.
13. Fischl, B. et al. (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 341-355.
14. Ghani, R. (2002) Combining labeled and unlabeled data for multi-class text categorization. In International Conference on Machine Learning, ACM, pp. 187-194.
15. Hinrichs, C. et al. (2009a) MKL for robust multi-modality ad classification. In Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II, pp. 786-794.
16. Hinrichs, C. et al. (2009b) Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage, 48, 138-149.
17. Kim, S. and Xing, E. (2010) Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. In International Conference on Machine Learning (ICML). pp. 352-359.
18. Kloft, M. et al. (2008) Non-sparse multiple kernel learning. In Proceedings of the NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels.
19. Krishnapuram, B. et al. (2005) Sparse multinomial logistic regression: fast algorithms and generalization bounds. In IEEE Trans. Pattern Anal. Mach. Intell., 27, 957-968.
20. Lanckriet, G. et al. (2004) Learning the kernel matrix with semidefinite programming. In JMLR, 5, 27-72.
21. Landau, S. et al. (2009) Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol. Aging, 32, 1207-1218.
22. Lee, S.-I. et al. (2006) Efficient l1 regularized logistic regression. In The 21st National Conference on Artificial Intelligence (AAAI), pp. 401.
23. Liu, J. et al. (2009) Large-scale sparse logistic regression. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 547-556.
24. Luo, D. et al. (2010) Towards structural sparsity: an explicit l2/l0 approach. In IEEE International Conference on Data Mining (ICDM), pp. 344-353.
25. Micchelli, C. et al. (2010) A family of penalty functions for structured sparsity. In Advances in Neural Information Processing System (NIPS), The MIT Press, pp. 1612-1623.
26. Nigam, K. et al. (2000) Text classification from labeled and unlabeled documents using em. Machine Learning, 39, 103-134.
27. Obozinski, G. et al. (2006) Multi-task feature selection. Technical report, Department of Statistics, University of California, Berkeley.
28. Obozinski, G. et al. (2010) Joint covariate selection and joint subspace selection for multiple classification problems. Stat. Comput., 20, 231-252.
29. 1,∞ regularization. In International Conference on Machine Learning (ICML), ACM, pp. 857-864.
30. Rakotomamonjy, A. et al. (2007) More efficiency in multiple kernel learning. In International Conference on Machine Learning (ICML), ACM, pp. 775-782.
31. Saykin, A.J. et al. (2010) Alzheimer's disease neuroimaging initiative biomarkers as quantitative phenotypes: genetics core aims, progress, and plans. Alzheimers Dement, 6, 265-273.
32. Shen, L. et al. (2010a) Sparse bayesian learning for identifying imaging biomarkers in AD prediction. Med. Image Comput. Comput. Assist. Interv., 13(Pt 3), 611-618.
33. Shen, L. et al. (2010b) Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage, 53, 1051-1063.
34. Sonnenburg, S. et al. (2006) Large scale multiple kernel learning. In JMLR, 7, 1531-1565.
35. Stonnington, C.M. et al. (2010) Predicting clinical scores frommagnetic resonance scans in alzheimer's disease. Neuroimage, 51, 1405-1413.
36. Sun, L. et al. (2009) Efficient recovery of jointly sparse vectors. In Advances in Neural Information Processing Systems (NIPS) 22, The MIT Press, pp. 1812-1820.
37. Suykens, J. et al. (2002) Least Squares Support Vector Machines, World Scientific, Singapore. (ISBN 981-238-151-1)
38. Tibshirani, R. (1996) Regression shrinkage and selection via the LASSO. J. R. Statist. Soc B., 58, 267-288.
39. Walhovd, K. et al. (2010) Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol. Aging, 31, 1107-1121.
40. Wang, H. et al. (2011) Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression. In The Proceedings of The 14th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2011), Lecture Notes in Computer Science (LNCS) 6893, Springer, pp. 115-123.
41. Wang, H. et al. (2012) Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics, 28, 229-237.
42. Yang, X. et al. (2009) Heterogeneous multitask learning with joint sparsity constraints. In Advances in Neural Information Processing System (NIPS), The MIT Press, pp. 2151-2159.
43. Ye, J. et al. (2008) Multi-class discriminant kernel learning via convex programming. In JMLR, 9, 719-758.
44. Yu, S. et al. (2010). L 2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinformatics, 11, 309.
45. Yuan, M. and Lin, Y. (2006) Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B, 68, 49C-67.
46. Zien, A. and Ong, C. (2007) Multiclass multiple kernel learning. In International Conference on Machine Learning (ICML), ACM, pp. 1191-1198.