Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging
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[1] M. Daly,et al. Centenarians and the genetics of longevity. , 2000, Results and problems in cell differentiation.
[2] JoAnn E. Manson,et al. Design of the Women's Health Initiative clinical trial and observational study. The Women's Health Initiative Study Group. , 1998, Controlled clinical trials.
[3] Lawrence M. Fagan,et al. Antimicrobial selection by a computer. A blinded evaluation by infectious diseases experts. , 1979, JAMA.
[4] S. Cummings,et al. Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group. , 1995, The New England journal of medicine.
[5] Rod D. Roscoe,et al. Genetic determinants of exceptional human longevity: insights from the Okinawa Centenarian Study , 2006, AGE.
[6] N. Barzilai,et al. Dissecting the Mechanisms Underlying Unusually Successful Human Health Span and Life Span. , 2015, Cold Spring Harbor perspectives in medicine.
[7] Felicitie C. Bell,et al. Life Tables for the United States Social Security Area 1900-2100 , 2002 .
[8] Parminder Raina,et al. Maelstrom Research guidelines for rigorous retrospective data harmonization , 2016, International journal of epidemiology.
[9] Evgeny Putin,et al. Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers , 2019, Scientific Reports.
[10] Laxmi Parida,et al. Enhancing Next‐Generation Sequencing‐Guided Cancer Care Through Cognitive Computing , 2017, The oncologist.
[11] Ajay K. Royyuru,et al. Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma , 2017, Neurology: Genetics.
[12] Dmitry Vetrov,et al. Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. , 2018, Molecular pharmaceutics.
[13] Jason H. Moore,et al. Evaluating recommender systems for AI-driven data science , 2019, ArXiv.
[14] S. G. Axline,et al. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. , 1975, Computers and biomedical research, an international journal.
[15] W. Kannel,et al. The Framingham Offspring Study. Design and preliminary data. , 1975, Preventive medicine.
[16] Alex Aliper,et al. Aging Chart: a community resource for rapid exploratory pathway analysis of age-related processes , 2016, Nucleic Acids Res..
[17] M. Krawczak,et al. Genetic investigation of FOXO3A requires special attention due to sequence homology with FOXO3B , 2012, European Journal of Human Genetics.
[18] A. Folsom,et al. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. , 1989, American journal of epidemiology.
[19] Kevin Leyton-Brown,et al. Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms , 2012, KDD.
[20] L. Partridge,et al. Facing up to the global challenges of ageing , 2018, Nature.
[21] Evgeny Putin,et al. Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations , 2018, The journals of gerontology. Series A, Biological sciences and medical sciences.
[22] Randal S. Olson,et al. PMLB: a large benchmark suite for machine learning evaluation and comparison , 2017, BioData Mining.
[23] Alex Zhavoronkov,et al. Artificial intelligence for aging and longevity research: Recent advances and perspectives , 2019, Ageing Research Reviews.
[24] Param Priya Singh,et al. The Genetics of Aging: A Vertebrate Perspective , 2019, Cell.
[25] J. Murabito,et al. The epidemiology of longevity and exceptional survival. , 2013, Epidemiologic reviews.
[26] Randal S. Olson,et al. TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning , 2016, AutoML@ICML.
[27] Eric J Topol,et al. High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.
[28] T. Dawber,et al. Epidemiological approaches to heart disease: the Framingham Study. , 1951, American journal of public health and the nation's health.
[29] Randal S. Olson,et al. Data-driven advice for applying machine learning to bioinformatics problems , 2017, PSB.
[30] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[31] R. Schneider. Comparison of age, sex, and incidence rates in human and canine breast cancer , 1970, Cancer.
[32] Yan Wang,et al. Novel loci and pathways significantly associated with longevity , 2016, Scientific Reports.
[33] Philip Whittick. Expert systems research in law enforcement , 1990, Comput. Law Secur. Rev..
[34] P. Allhoff,et al. The Framingham Offspring Study , 1991 .
[35] Ricardo Macarrón,et al. Design and Implementation of High Throughput Screening Assays , 2011, Molecular biotechnology.
[36] A. Zhavoronkov,et al. Methods for Structuring Scientific Knowledge from Many Areas Related to Aging Research , 2011, PloS one.
[37] S. Cummings,et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study--a large observational study of the determinants of fracture in older men. , 2005, Contemporary clinical trials.
[38] Jennifer Chu-Carroll,et al. Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..
[39] Polina Mamoshina,et al. Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease , 2015, Aging.
[40] Aaron Klein,et al. Efficient and Robust Automated Machine Learning , 2015, NIPS.
[41] A. Zhavoronkova,et al. Artificial intelligence for aging and longevity research , 2018 .
[42] Lynette Ekunwe,et al. Study design for genetic analysis in the Jackson Heart Study. , 2005, Ethnicity & disease.
[43] Lana X. Garmire,et al. More Is Better: Recent Progress in Multi-Omics Data Integration Methods , 2017, Front. Genet..
[44] Evgeny Putin,et al. Deep biomarkers of human aging: Application of deep neural networks to biomarker development , 2016, Aging.
[45] Toshiko Tanaka,et al. GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy. , 2015, The journals of gerontology. Series A, Biological sciences and medical sciences.
[46] Thomas Meitinger,et al. A genome-wide association study confirms APOE as the major gene influencing survival in long-lived individuals , 2011, Mechanisms of Ageing and Development.
[47] Laxmi Parida,et al. Watson for Genomics: Moving Personalized Medicine Forward. , 2016, Trends in cancer.
[48] B. Kennedy,et al. Ageing: A midlife longevity drug? , 2009, Nature.
[49] V. Gudnason,et al. Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics. , 2007, American journal of epidemiology.
[50] P. Sebastiani,et al. Four Genome-Wide Association Studies Identify New Extreme Longevity Variants , 2017, The journals of gerontology. Series A, Biological sciences and medical sciences.
[51] Sang-Goo Lee,et al. Using DNA Methylation Profiling to Evaluate Biological Age and Longevity Interventions. , 2017, Cell metabolism.
[52] A. Budovsky,et al. Wide‐scale comparative analysis of longevity genes and interventions , 2017, Aging cell.
[53] R. Gillies,et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study , 2018, PLoS medicine.
[54] R. Pignolo. Exceptional Human Longevity , 2019, Mayo Clinic proceedings.
[55] Anne E Carpenter,et al. Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.
[56] Paola Sebastiani,et al. Human longevity and common variations in the LMNA gene: a meta‐analysis , 2012, Aging cell.
[57] H. Haenssle,et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.
[58] N. Cox,et al. Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS , 2010, PLoS genetics.
[59] Michael Petrascheck,et al. The DrugAge database of aging‐related drugs , 2017, Aging cell.
[60] T. Perls,et al. The Genetics of Exceptional Human Longevity , 2002, Journal of molecular neuroscience : MN.
[61] R. Kronmal,et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. , 2002, American journal of epidemiology.
[62] Randal S. Olson,et al. Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming , 2017, GECCO.
[63] M. Kaeberlein. How healthy is the healthspan concept? , 2018, GeroScience.
[64] Paola Sebastiani,et al. Health and function of participants in the Long Life Family Study: A comparison with other cohorts , 2011, Aging.
[65] Paola Sebastiani,et al. A family longevity selection score: ranking sibships by their longevity, size, and availability for study. , 2009, American journal of epidemiology.
[66] B. Kennedy,et al. The genetics of ageing: insight from genome‐wide approaches in invertebrate model organisms , 2008, Journal of internal medicine.
[67] D. Melzer,et al. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants , 2017, Aging.
[68] V. Gladyshev,et al. A Disease or Not a Disease? Aging As a Pathology. , 2016, Trends in molecular medicine.
[69] A. Zhavoronkov,et al. Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification , 2018, Front. Genet..
[70] Daniel W. Jones,et al. American Heart Association Cardiovascular Genome-Phenome Study , 2015, Circulation.
[71] P. Sebastiani,et al. Limitations and risks of meta-analyses of longevity studies , 2017, Mechanisms of Ageing and Development.
[72] Andrey Alekseenko,et al. Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of COX7A1 in embryonic and cancer cells , 2017, Oncotarget.
[73] Jeffrey Dean,et al. Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.
[74] R. Kronmal,et al. The Cardiovascular Health Study: design and rationale. , 1991, Annals of epidemiology.
[75] S B Hulley,et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. , 1988, Journal of clinical epidemiology.
[76] J. Cole,et al. Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases , 2018, BioEssays : news and reviews in molecular, cellular and developmental biology.
[77] P. Sebastiani,et al. Meta-analysis of genetic variants associated with human exceptional longevity , 2013, Aging.
[78] Evgeny Putin,et al. In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state , 2016, Aging.
[79] Sergey Nikolenko,et al. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. , 2017, Molecular pharmaceutics.
[80] Qiong Yang,et al. The Third Generation Cohort of the National Heart, Lung, and Blood Institute's Framingham Heart Study: design, recruitment, and initial examination. , 2007, American journal of epidemiology.
[81] P. Cawthon,et al. Overview of recruitment for the osteoporotic fractures in men study (MrOS). , 2005, Contemporary clinical trials.
[82] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[83] S. Deweerdt. Comparative biology: Looking for a master switch , 2012, Nature.
[84] Kathleen F. Kerr,et al. Genetic Diversity and Association Studies in US Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos. , 2016, American journal of human genetics.