ADHD Diagnosis from Multiple Data Sources with Batch Effects

Frontiers in Systems Neuroscience

Volumn , Issue SEPTEMBER, 2012, Pages 1-22

  Olivetti, Emanuele ^a,b   Greiner, Susanne ^a,b   Avesani, Paolo ^a,b  

^a FONDAZIONE BRUNO KESSLER   (Italy)

^b UNIVERSITY OF TRENTO   (Italy)

Author keywords

ADHD; Batch effect; Bayesian hypothesis testing; Dissimilarity space; Multivariate pattern classification

Indexed keywords

BIOLOGICAL MARKER;

ARTICLE; ATTENTION DEFICIT DISORDER; BATCH EFFECT; CLASSIFICATION; CLASSIFIER; DATA ANALYSIS; DIAGNOSTIC ACCURACY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; INFORMATION PROCESSING; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOPHYSIOLOGY; PHENOTYPE; PREDICTION; ADULT; AUTOMATION; BAYES THEOREM; CHILD; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; DATA PROCESSING; HUMAN; SCHOOL CHILD; VALIDATION PROCESS;

EID: 84867125436     PISSN: 16625137     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnsys.2012.00070     Document Type: Article

References (24)

1. J. Ashburner and K. J. Friston. Voxel-based morphometry-the methods. NeuroImage, 11(6 Pt 1):805-821, June 2000.
2. Maria F. Balcan, Avrim Blum, and Santosh Vempala. Kernels as features: On kernels, margins, and low-dimensional mappings. Mach. Learn., 65(1):79-94, October 2006.
3. Maria-Florina Balcan, Avrim Blum, and Nathan Srebro. A theory of learning with similarity functions. Machine Learning, 72(1):89-112, August 2008.
4. James O. Berger and Luis R. Pericchi. The Intrinsic Bayes Factor for Model Selection and Prediction. Journal of the American Statistical Association, 91(433):109-122, 1996.
5. Michael J. Boedigheimer, Russell D. Wolfinger, Michael B. Bass, Pierre R. Bushel, Jeff W. Chou, Matthew Cooper, J. Christopher Corton, Jennifer Fostel, Susan Hester, Janice S. Lee, Fenglong Liu, Jie Liu, Hui-Rong R. Qian, John Quackenbush, Syril Pettit, and Karol L. Thompson. Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC genomics, 9:285+, June 2008.
6. Leo Breiman. Random Forests. Machine Learning, 45(1):5-32, October 2001.
7. George Casella and Elias Moreno. Assessing Robustness of Intrinsic Tests of Independence in Two-Way Contingency Tables. Journal of the American Statistical Association, 104(487):1261-1271, September 2009.
8. Chao Chen, Kay Grennan, Judith Badner, Dandan Zhang, Elliot Gershon, Li Jin, and Chunyu Liu. Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods. PLoS ONE, 6(2):e17238+, February 2011.
9. Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, and Luca Cazzanti. Similarity-based Classification: Concepts and Algorithms. J. Mach. Learn. Res., 10:747-776, June 2009.
10. Murat Dundar, Balaji Krishnapuram, Jinbo Bi, and R. Bharat Rao. Learning classifiers when the training data is not IID. In Proceedings of the 20th international joint conference on Artifical intelligence, pages 756-761, San Francisco, CA, USA, 2007. Morgan Kaufmann Publishers Inc.
11. Pierre Geurts, Damien Ernst, and Louis Wehenkel. Extremely randomized trees. Machine Learning, 63(1):3-42, April 2006.
12. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). Springer, 2nd ed. 2009. corr. 3rd printing 5th printing. edition, February 2009.
13. Hemant Ishwaran. Inference for the random effects in Bayesian generalized linear mixed models. In Proceedings of the Bayesian Statistical Science Section, pages 1-10. ASA, 2000.
14. Robert E. Kass and Adrian E. Raftery. Bayes Factors. Journal of the American Statistical Association, 90(430):773-795, June 1995.
15. Nikolaus Kriegeskorte, Marieke Mur, Douglas A. Ruff, Roozbeh Kiani, Jerzy Bodurka, Hossein Esteky, Keiji Tanaka, and Peter A. Bandettini. Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey. Neuron, 60(6):1126-1141, December 2008.
16. Charles E. McCulloch, Shayle R. Searle, and John M. Neuhaus. Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics). Wiley-Interscience, 2 edition, June 2008.
17. Emanuele Olivetti, Susanne Greiner, and Paolo Avesani. Induction in Neuroscience with Classification: Issues and Solutions. Lecture Notes in Compuer Science. Springer, 2012.
18. Emanuele Olivetti, Thien B. Nguyen, and Eleftherios Garyfallidis. The Approximation of the Dissimilarity Projection. In IEEE International Workshop on Pattern Recognition in NeuroImaging. IEEE, 2012.
19. Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011.
20. E. Pekalska, R. Duin, and P. Paclik. Prototype selection for dissimilarity-based classifiers. Pattern Recognition, 39(2):189-208, February 2006.
21. Elzbieta Pekalska and Robert P. W. Duin. The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence). World Scientific Publishing Company, December 2005.
22. Elzbieta Pekalska, Pavel Paclik, and Robert P. W. Duin. A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res., 2:175-211, 2002.
23. Stephen M. Smith, Peter T. Fox, Karla L. Miller, David C. Glahn, Mickle M. Fox, Clare E. Mackay, Nicola Filippini, Kate E. Watkins, Roberto Toro, Angela R. Laird, and Christian F. Beckmann. Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31):13040-13045, August 2009.
24. Yufeng Zang, Tianzi Jiang, Yingli Lu, Yong He, and Lixia Tian. Regional homogeneity approach to fMRI data analysis. NeuroImage, 22(1):394-400, May 2004.