Big data analytics for personalized medicine.
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[1] M. Papadopoulos,et al. Targeted Perfusion Therapy in Spinal Cord Trauma , 2020, Neurotherapeutics.
[2] J. Loscalzo,et al. The application of big data to cardiovascular disease: paths to precision medicine. , 2020, The Journal of clinical investigation.
[3] H. U. Zacharias,et al. A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study , 2019, Scientific Reports.
[4] G. Warren. Mitigating the adverse health effects and costs associated with smoking after a cancer diagnosis. , 2019, Translational lung cancer research.
[5] Eric J Topol,et al. High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.
[6] Christopher Ré,et al. Snorkel: Rapid Training Data Creation with Weak Supervision , 2017, Proc. VLDB Endow..
[7] Rishikesan Kamaleswaran,et al. PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform , 2019, IEEE Journal of Biomedical and Health Informatics.
[8] Arpan Kumar Kar,et al. Big data with cognitive computing: A review for the future , 2018, Int. J. Inf. Manag..
[9] Claude Thermes,et al. The Third Revolution in Sequencing Technology. , 2018, Trends in genetics : TIG.
[10] Michalis E. Zervakis,et al. A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals , 2018, Comput. Biol. Medicine.
[11] Nicholas Genes,et al. From smartphone to EHR: a case report on integrating patient-generated health data , 2018, npj Digital Medicine.
[12] Chandra L. Theesfeld,et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk , 2018, Nature Genetics.
[13] Yeong Shiong Chiew,et al. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them , 2018, BioMedical Engineering OnLine.
[14] Abhinav Nellore,et al. Cloud computing for genomic data analysis and collaboration , 2018, Nature Reviews Genetics.
[15] Jeffrey Dean,et al. Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.
[16] Thierry Kogej,et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.
[17] Laxmi Parida,et al. Enhancing Next‐Generation Sequencing‐Guided Cancer Care Through Cognitive Computing , 2017, The oncologist.
[18] Alioune Ngom,et al. A review on machine learning principles for multi-view biological data integration , 2016, Briefings Bioinform..
[19] Cory Y. McLean,et al. Creating a universal SNP and small indel variant caller with deep neural networks , 2016, bioRxiv.
[20] Terry Anthony Byrd,et al. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations , 2018 .
[21] Scott Spangler,et al. Artificial intelligence in neurodegenerative disease research : use of IBM Watson to identify additional RNA ‐ binding proteins , 2017 .
[22] Ellen Heitzer,et al. The potential of liquid biopsies for the early detection of cancer , 2017, npj Precision Oncology.
[23] I. Norstedt,et al. Enabling personalized medicine in Europe by the European Commission's funding activities. , 2017, Personalized medicine.
[24] Heung-Il Suk,et al. Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.
[25] Bálint Antal,et al. Image Data Resource: a bioimage data integration and publication platform , 2017, Nature Methods.
[26] Anirban Basu,et al. On blockchain-based anonymized dataset distribution platform , 2017, 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA).
[27] Anne E Carpenter,et al. Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.
[28] F. Arnaud,et al. From core referencing to data re-use: two French national initiatives to reinforce paleodata stewardship (National Cyber Core Repository and LTER France Retro-Observatory) , 2017 .
[29] A. Pang,et al. Combination of short-read, long-read, and optical mapping assemblies reveals large-scale tandem repeat arrays with population genetic implications , 2017, Genome research.
[30] H. Rehm. Evolving health care through personal genomics , 2017, Nature Reviews Genetics.
[31] Ronald N. Kalla,et al. IBM Power9 Processor Architecture , 2017, IEEE Micro.
[32] Viktor K. Jirsa,et al. Individual brain structure and modelling predict seizure propagation , 2017, Brain : a journal of neurology.
[33] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[34] Peter V. Coveney,et al. Multiscale computing in the exascale era , 2016, J. Comput. Sci..
[35] R. Appel,et al. Funding knowledgebases: Towards a sustainable funding model for the UniProt use case , 2017, F1000Research.
[36] Alfonso Valencia,et al. The BLUEPRINT Data Analysis Portal. , 2016, Cell systems.
[37] Charles E. Cook,et al. Identifying ELIXIR Core Data Resources , 2016, F1000Research.
[38] Chuan-Ming Liu,et al. Big data stream computing in healthcare real-time analytics , 2016, 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).
[39] Rajkumar Buyya,et al. Ensuring Security and Privacy Preservation for Cloud Data Services , 2016, ACM Comput. Surv..
[40] S. Marjanovic,et al. Population-scale sequencing and the future of genomic medicine , 2016 .
[41] Ying Chen,et al. IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. , 2016, Clinical therapeutics.
[42] Ivo D Dinov,et al. Volume and Value of Big Healthcare Data. , 2016, Journal of medical statistics and informatics.
[43] Jon R Lorsch,et al. Perspective: Sustaining the big-data ecosystem , 2015, Nature.
[44] Gregory Ditzler,et al. Multi-Layer and Recursive Neural Networks for Metagenomic Classification , 2015, IEEE Transactions on NanoBioscience.
[45] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[46] M. Schatz,et al. Big Data: Astronomical or Genomical? , 2015, PLoS biology.
[47] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[48] F. Collins,et al. A new initiative on precision medicine. , 2015, The New England journal of medicine.
[49] Michelle Dunn,et al. The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data , 2014, J. Am. Medical Informatics Assoc..
[50] Mark D. Lim,et al. Consortium Sandbox: Building and Sharing Resources , 2014, Science Translational Medicine.
[51] R. Kitchin,et al. Big Data, new epistemologies and paradigm shifts , 2014, Big Data Soc..
[52] Mona Singh,et al. Computational solutions for omics data , 2013, Nature Reviews Genetics.
[53] Alan Agresti,et al. Categorical Data Analysis , 2003 .
[54] Alan L. Rector,et al. Granularity, scale and collectivity: When size does and does not matter , 2006, J. Biomed. Informatics.
[55] M. Cox,et al. Application-controlled demand paging for out-of-core visualization , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).