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Presentation Topics

  1. Statistical inference for noisy nonlinear ecological dynamic systems. SN Wood, Nature, 2010
  2. The spread of obesity in a large social network over 32 years. NA Christakis and JH Fowler, New England Journal of Medicine, 2007
  3. Nonlinear dimensionality reduction by locally linear embedding. ST Roweis, LK Saul, Science, 2000
  4. A Bayesian approach to filtering junk e-mail. M. Sahami et al., Learning for Text Categorization, 1998
  5. Regression shrinkage and selection via the lasso. R Tibshirani, Journal of the Royal Statistical Society. Series B, 1996
  6. Bayesian integration in sensorimotor learning. KP Körding and DM Wolpert, Nature, 2004
  7. A Bayesian approach to unsupervised one-shot learning of object categories. L Fei-Fei, R Fergus, P Perona, Ninth IEEE International Conference on Computer Vision (ICCV'03), 2003
  8. Learning Object Categories from Google's Image Search. R Fergus, L Fei-Fei, P Perona, A Zisserman, Tenth IEEE International Conference on Computer Vision (ICCV'05), 2005
  9. Bayesian ranking of biochemical system models. V Vyshemirsky and MA Girolami, Bioinformatics, 2008
  10. Phylogenetic inference using whole genomes. B Rannala and Z Yang, Annual Review of Genomics and Human Genetics, 2008
  11. Greenhouse-gas emission targets for limiting global warming to 2 C M Meinshausen et al. Nature, 2009
  12. Causal protein-signaling networks derived from multiparameter single-cell data. K Sachs et al., Science, 2005
  13. Sample selection bias as a specification error. JJ Heckman, Econometrica, 1979
  14. Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival. Nicolau, et al. PNAS, 108(17):7265–7270, 2011. doi:10.1073/pnas.1102826109

    More! Papers on fMRI "Mind Reading"
  15. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Haxby, et al. Science 293(5539), 2425–30, 2001. doi:10.1126/science.1063736
  16. Training fMRI classifiers to discriminate cognitive states across multiple subjects. Wang et al. Proc. of The 17th Annual Conference on Neural Information Processing Systems, 2003
  17. Learning to Decode Cognitive States from Brain Images. Mitchell et al. Machine Learning, 57(1/2), 145–175, 2004. doi:10.1023/B:MACH.0000035475.85309.1b
  18. Decoding the visual and subjective contents of the human brain. Kamitani & Tong. Nature Neuroscience, 8(5), 679–85, 2005. doi:10.1038/nn1444
  19. Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Mourão-Miranda et al. NeuroImage, 28(4), 980–95, 2005. doi:10.1016/j.neuroimage.2005.06.070
  20. Predicting human brain activity associated with the meanings of nouns. Mitchell, et al. Science, 320(5880), 1191–5, 2008. doi:10.1126/science.1152876.
  21. Using FMRI brain activation to identify cognitive states associated with perception of tools and dwellings. Shinkareva, et al. PloS one, 3(1), e1394, 2008. doi:10.1371/journal.pone.0001394
  22. Unconscious determinants of free decisions in the human brain. Soon, et al. Nature neuroscience, 11(5), 543–5, 2008. doi:10.1038/nn.2112
  23. Recruitment of an area involved in eye movements during mental arithmetic. Knops et al. Science, 324(5934), 1583–5, 2009. doi:10.1126/science.1171599
  24. Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Marquand et al. NeuroImage, 49(3), 2178–89, 2010. doi:10.1016/j.neuroimage.2009.10.072
  25. Reproducibility distinguishes conscious from nonconscious neural representations. Schurger et al. Science 327(5961), 97–9, 2010. doi:10.1126/science.1180029

    More! Papers on intersubject classification using MRI
  26. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Ecker et al. NeuroImage, 49(1), 44–56. 2001. doi:10.1016/j.neuroimage.2009.08.024
  27. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Plant et al. NeuroImage, 50(1), 162–74, 2010. doi:10.1016/j.neuroimage.2009.11.046
  28. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Davatzikos et al. Neurobiology of aging, 32(12), 2322.e19–27, 2011. doi:10.1016/j.neurobiolaging.2010.05.023
  29. Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND. Vemuri, et al. NeuroImage, 55(2), 522–31, 2011. doi:10.1016/j.neuroimage.2010.12.073
  30. Diagnostic neuroimaging across diseases. Klöppel, et al. NeuroImage, 61(2), 457–63, 2012. doi:10.1016/j.neuroimage.2011.11.002
  31. Prediction of Individual Brain Maturity Using fMRI. Dosenbach et al. Science, 329(5997), 1358–1361, 2010. doi:10.1126/science.1194144

  32. Margin based feature selection - theory and algorithms. Gilad-Bachrach et al. Twenty-first international conference on Machine learning - ICML ’04 (p. 43), 2004. New York, New York, USA: ACM Press. doi:10.1145/1015330.1015352
  33. Decoding Human Cytomegalovirus. Stern-Ginossar et al. Science 338(6110):1088-1093, 2012. doi:10.1126/science.1227919
  34. Teamwork: improved eQTL mapping using combinations of machine learning methods. Ackermann, et al. PloS one, 7(7), e40916, 2012. doi:10.1371/journal.pone.0040916
  35. Human gut microbiome viewed across age and geography. Yatsunenko et al. Nature, 486(7402), 222–7, 2012. doi:10.1038/nature11053
  36. A perceptual metric for photo retouching. Kee & Farid. Proceedings of the National Academy of Sciences, 108(50), 19907–12, 2011. doi:10.1073/pnas.1110747108
  37. Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Kharchenko et al. Nature 471:480–485, 2011. doi:10.1038/nature09725
  38. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Neumann et al. Nature 464, 721-727, 2010. doi:10.1038/nature08869
  39. Probabilistic assessment of sea level during the last interglacial stage. Kopp et al. Nature 462:863-867, 2009. doi:10.1038/nature08686
  40. Combinatorial binding predicts spatio-temporal cis-regulatory activity. Zinzen, et al. Nature 462:65-70, 2009. doi:10.1038/nature08531
  41. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Krogan et al. Nature 440, 637-643, 2006. doi:10.1038/nature04670
  42. Quantifying the Relationships among Drug Classes. Hert et al. J. Chem. Inf. Model., 48(4):755–765, 2008. doi:10.1021/ci8000259

    One more Mind Reading paper!
  43. Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Miyawaki et al. Neuron, 60(5), 915–29, 2008. doi:10.1016/j.neuron.2008.11.004 New!

    No! Wait! More Mind Reading papers!
  44. Predicting the orientation of invisible stimuli from activity in primary visual cortex. Haynes & Rees. Nature Neurosci. 8, 686–691, 2005.
  45. Predicting the Stream of Consciousness from Activity in Human Visual Cortex. Haynes & Rees. Current Biology, 15(14):1301-1307, 2005. doi:10.1016/j.cub.2005.06.026
  46. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Davatzikos, C. et al. Neuroimage 28, 663–668, 2005.
  47. Real-time decoding and training of attention. deBettencourt et al. Journal of Vision, 12(9):377, 2012. doi: 10.1167/12.9.377