Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework
暂无分享,去创建一个
Arthur W. Toga | Fengzhu Sun | Kaida Ning | Bo Chen | Lu Zhao | A. Toga | Fengzhu Sun | Bo Chen | Kaida Ning | Lu Zhao | William J Matloff | Zachary Hobel | Will Matloff | Zachary B. Hobel | William Matloff
[1] J. Whitwell,et al. Alzheimer's disease neuroimaging , 2018, Current opinion in neurology.
[2] K. Hao,et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease , 2017, Nature Neuroscience.
[3] Yan Liu,et al. Detecting Statistical Interactions from Neural Network Weights , 2017, ICLR.
[4] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[5] Simona Maria Brambati,et al. Altered Gray Matter Structural Covariance Networks in Early Stages of Alzheimer's Disease. , 2016, Cerebral cortex.
[6] Marco Tulio Ribeiro,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, HLT-NAACL Demos.
[7] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[8] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] M. Gill,et al. Common polygenic variation enhances risk prediction for Alzheimer's disease. , 2015, Brain : a journal of neurology.
[10] Hongtu Zhu,et al. Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data. , 2015, Journal of Alzheimer's disease : JAD.
[11] Michael W. Weiner,et al. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception , 2015, Alzheimer's & Dementia.
[12] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[13] Matthew L Senjem,et al. Age, Sex, and APOE ε4 Effects on Memory, Brain Structure, and β-Amyloid Across the Adult Life Span. , 2015, JAMA neurology.
[14] P. Coupé,et al. Structural imaging biomarkers of Alzheimer's disease: predicting disease progression , 2015, Neurobiology of Aging.
[15] Andrea Vedaldi,et al. MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.
[16] Dinggang Shen,et al. Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction , 2014, Front. Aging Neurosci..
[17] C. Jack,et al. Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50–89 years: a cross-sectional study , 2014, The Lancet Neurology.
[18] Perry G. Ridge,et al. Population-based Analysis of Alzheimer’s Disease Risk Alleles Implicates Genetic Interactions , 2014, Biological Psychiatry.
[19] Norbert Schuff,et al. Locally linear embedding (LLE) for MRI based Alzheimer's disease classification , 2013, NeuroImage.
[20] J. Trojanowski,et al. Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers , 2013, NeuroImage: Clinical.
[21] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[22] Nick C Fox,et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.
[23] Francis J McMahon,et al. In vivo radioligand binding to translocator protein correlates with severity of Alzheimer's disease. , 2013, Brain : a journal of neurology.
[24] A. Simmons,et al. Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment , 2013, Psychiatry Research: Neuroimaging.
[25] M. Jorge Cardoso,et al. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment☆ , 2013, NeuroImage: Clinical.
[26] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[27] Bruce Fischl,et al. FreeSurfer , 2012, NeuroImage.
[28] P. Scheltens,et al. Atrophy of medial temporal lobes on MRI in “probable” Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates , 2012, Journal of Neurology, Neurosurgery & Psychiatry.
[29] A. Mechelli,et al. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.
[30] Mark E. Schmidt,et al. The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception , 2012, Alzheimer's & Dementia.
[31] J. Trojanowski,et al. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification , 2011, Neurobiology of Aging.
[32] J. Marchini,et al. Genotype Imputation with Thousands of Genomes , 2011, G3: Genes | Genomes | Genetics.
[33] D. Rueckert,et al. Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease , 2011, PloS one.
[34] Hojjat Adeli,et al. Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence , 2011, Journal of Neuroscience Methods.
[35] C. Jack,et al. Alzheimer's Disease Neuroimaging Initiative (ADNI) , 2010, Neurology.
[36] Karin Bammann,et al. Neural networks for modeling gene-gene interactions in association studies , 2009, BMC Genetics.
[37] S. Resnick,et al. Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index. , 2009, Brain : a journal of neurology.
[38] Li Shen,et al. Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort , 2009, Current Alzheimer research.
[39] P. Donnelly,et al. A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.
[40] M. Weiner,et al. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* , 2009, Brain : a journal of neurology.
[41] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[42] Christian Büchel,et al. Contributions of occipital, parietal and parahippocampal cortex to encoding of object-location associations , 2005, Neuropsychologia.
[43] Russell G. Death,et al. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .
[44] F. Collette,et al. Alzheimer' Disease as a Disconnection Syndrome? , 2003, Neuropsychology Review.
[45] M. Gevrey,et al. Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .
[46] A. D. Roses,et al. Association of apolipoprotein E allele €4 with late-onset familial and sporadic Alzheimer’s disease , 2006 .
[47] P. Scheltens,et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. , 1992, Journal of neurology, neurosurgery, and psychiatry.
[48] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[49] D. Sharp,et al. The role of the posterior cingulate cortex in cognition and disease. , 2014, Brain : a journal of neurology.
[50] B. Ripley. Pattern Recognition and Neural Networks , 1996 .
[51] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .