Semi-supervised learning of the electronic health record for phenotype stratification
暂无分享,去创建一个
[1] Guergana K. Savova,et al. Semi-supervised Learning for Phenotyping Tasks , 2015, AMIA.
[2] Juan Carlos Fernández,et al. Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms , 2014, Ann. Oper. Res..
[3] Eric M. Morrow,et al. A Genome-wide Association Study of Autism Using the Simons Simplex Collection: Does Reducing Phenotypic Heterogeneity in Autism Increase Genetic Homogeneity? , 2015, Biological Psychiatry.
[4] Aurélie Labbe,et al. Symptom dimensions as alternative phenotypes to address genetic heterogeneity in schizophrenia and bipolar disorder , 2012, European Journal of Human Genetics.
[5] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Melissa A. Basford,et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.
[7] Brett Beaulieu-Jones. Denoising Autoencoders for Phenotype Stratification (DAPS): Preprint Release , 2016 .
[8] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[9] Sridhar Ramaswamy,et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer , 2014, Science.
[10] Benjamin S. Glicksberg,et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity , 2015, Science Translational Medicine.
[11] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[12] John Shawe-Taylor,et al. Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning , 2012, PloS one.
[13] Alessandra Renieri,et al. FOXG1 is responsible for the congenital variant of Rett syndrome. , 2008, American journal of human genetics.
[14] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[15] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[16] C. Dion,et al. Negative, psychoticism, and disorganized dimensions in patients with familial schizophrenia or bipolar disorder: continuity and discontinuity between the major psychoses. , 1995, The American journal of psychiatry.
[18] R. Steinbrook. Health care and the American Recovery and Reinvestment Act. , 2009, The New England journal of medicine.
[19] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] L KREYBERG,et al. Histological lung cancer types. A morphological and biological correlation. , 1962, Acta pathologica et microbiologica Scandinavica. Supplement.
[21] F. Civeira,et al. Guidelines for the diagnosis and management of heterozygous familial hypercholesterolemia. , 2004, Atherosclerosis.
[22] Dana C. Crawford,et al. The detection and characterization of pleiotropy: discovery, progress, and promise , 2016, Briefings Bioinform..
[23] Manolis Kellis,et al. Deep learning for regulatory genomics , 2015, Nature Biotechnology.
[24] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[25] Jeff Shrager,et al. A Novel Classification of Lung Cancer into Molecular Subtypes , 2012, PloS one.
[26] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[27] Long Wang,et al. Win-Stay-Lose-Learn Promotes Cooperation in the Spatial Prisoner's Dilemma Game , 2012, PloS one.
[28] Geoffrey E. Hinton,et al. Stochastic Neighbor Embedding , 2002, NIPS.
[29] Blaine A. Price,et al. Remote electronic examinations: student experiences , 2002, Br. J. Educ. Technol..
[30] Charles A Powell,et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society: international multidisciplinary classification of lung adenocarcinoma: executive summary. , 2011, Proceedings of the American Thoracic Society.
[31] Razvan Pascanu,et al. Theano: A CPU and GPU Math Compiler in Python , 2010, SciPy.
[32] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[33] Johann S. Hawe,et al. Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression , 2014, Nature Biotechnology.
[34] George Hripcsak,et al. Coding Neuroradiology Reports for the Northern Manhattan Stroke Study: A Comparison of Natural Language Processing and Manual Review , 2000, Comput. Biomed. Res..
[35] C. Mountain,et al. Revisions in the International System for Staging Lung Cancer. , 1997, Chest.
[36] Christoph Wick,et al. Augmented Reality Simulator for Training in Two-Dimensional Echocardiography , 2000, Comput. Biomed. Res..
[37] Aurélie Labbe,et al. Using disease symptoms to improve detection of linkage under genetic heterogeneity , 2008, Genetic epidemiology.
[38] Razvan Pascanu,et al. Theano: new features and speed improvements , 2012, ArXiv.
[39] Enhong Chen,et al. Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.
[40] M. Brilliant,et al. A PheWAS approach in studying HLA-DRB1*1501 , 2013, Genes and Immunity.
[41] Tshilidzi Marwala,et al. Missing data: A comparison of neural network and expectation maximization techniques , 2007 .
[42] B. Mohammadi,et al. ALSFRS-R score and its ratio: A useful predictor for ALS-progression , 2008, Journal of the Neurological Sciences.
[43] Masahiro Tsuboi,et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma , 2011, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.
[44] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[45] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.
[46] Casey S. Greene,et al. Unsupervised Feature Construction and Knowledge Extraction from Genome-Wide Assays of Breast Cancer with Denoising Autoencoders , 2014, Pacific Symposium on Biocomputing.
[47] M. Alda,et al. The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases , 2013, PloS one.
[48] Naomi R. Wray,et al. Assessment of Response to Lithium Maintenance Treatment in Bipolar Disorder: A Consortium on Lithium Genetics (ConLiGen) Report , 2013, PloS one.
[49] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[50] Melissa A. Basford,et al. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. , 2011, American journal of human genetics.
[51] Ya Zhang,et al. A Machine Learning-based Framework to Identify Type 2 Diabetes through Electronic Health Records , 2016, bioRxiv.