Artificial intelligence, physiological genomics, and precision medicine.

Big data are a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning toward machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records. However, relevance and accuracy of the data are as important as quantity of data in the advancement of ML for precision medicine. For common diseases, physiological genomic readouts in disease-applicable tissues may be an effective surrogate to measure the effect of genetic and environmental factors and their interactions that underlie disease development and progression. Disease-applicable tissue may be difficult to obtain, but there are important exceptions such as kidney needle biopsy specimens. As AI continues to advance, new analytical approaches, including those that go beyond data correlation, need to be developed and ethical issues of AI need to be addressed. Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.

[1]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[2]  E H Shortliffe,et al.  Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[3]  J. Kai,et al.  Can machine-learning improve cardiovascular risk prediction using routine clinical data? , 2017, PloS one.

[4]  Eric Horvitz.  AI, people, and society , 2017, Science.

[5]  Peter Norvig,et al.  The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.

[6]  Andrey Kan,et al.  Machine learning applications in cell image analysis , 2017, Immunology and cell biology.

[7]  Peng-Yuan Liu,et al.  miR‐29 contributes to normal endothelial function and can restore it in cardiometabolic disorders , 2018, EMBO molecular medicine.

[8]  J. Ribeiro,et al.  Personalized or Precision Medicine? The Example of Cystic Fibrosis , 2017, Front. Pharmacol..

[9]  Z. Obermeyer,et al.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.

[10]  Purushottam W. Laud,et al.  Urinary Metabolites Associated with Blood Pressure on a Low- or High-Sodium Diet , 2017, Theranostics.

[11]  K. Iczkowski,et al.  Tissue-Specific MicroRNA Expression Patterns in Four Types of Kidney Disease. , 2017, Journal of the American Society of Nephrology : JASN.

[12]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[13]  Kevin Waugh,et al.  DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker , 2017, ArXiv.

[14]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[15]  P. Halloran,et al.  Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. , 2017, JCI insight.

[16]  Yang Wang,et al.  Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. , 2018, Cancer genomics & proteomics.

[17]  John F. McDonald,et al.  Open source machine-learning algorithms for the prediction of optimal cancer drug therapies , 2017, PloS one.

[18]  J. Beckmann,et al.  Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities , 2016, Genome Medicine.

[19]  Integrative pathway knowledge bases as a tool for systems molecular medicine. , 2007, Physiological genomics.

[20]  Steve Halligan,et al.  Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: A discussion and proposal for an alternative approach , 2015, European Radiology.

[21]  Andrew D. Selbst,et al.  Big Data's Disparate Impact , 2016 .

[22]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[23]  Lennart Martens,et al.  Machine learning applications in proteomics research: How the past can boost the future , 2014, Proteomics.

[24]  Gerald Tesauro,et al.  Temporal difference learning and TD-Gammon , 1995, CACM.

[25]  C. Betsholtz,et al.  Transcriptomic and Proteomic Profiling Provides Insight into Mesangial Cell Function in IgA Nephropathy. , 2017, Journal of the American Society of Nephrology : JASN.

[26]  Joeky T Senders,et al.  REVIEWARTICLE-NEUROSURGICALTECHNIQUES An introduction and overview of machine learning in neurosurgical care , 2017 .

[27]  P. Balaram,et al.  Nuclear magnetic resonance studies of the interaction of peptides and hormones with bovine neurophysin. , 1973, Biochemistry.

[28]  Murray Campbell,et al.  Deep Blue , 2002, Artif. Intell..

[29]  Abdul Hanan Abdullah,et al.  A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care , 2017, Journal of Medical Systems.

[30]  Eric W. Brown,et al.  Making Watson fast , 2012, IBM J. Res. Dev..

[31]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[32]  D. Hassabis,et al.  Neuroscience-Inspired Artificial Intelligence , 2017, Neuron.

[33]  Jonathan Schaeffer,et al.  CHINOOK: The World Man-Machine Checkers Champion , 1996, AI Mag..

[34]  Lawrence B. Holder,et al.  Machine learning for epigenetics and future medical applications , 2017, Epigenetics.

[35]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[36]  Mingyu Liang,et al.  Hypertension: From GWAS to functional genomics-based precision medicine , 2017, Nature Reviews Nephrology.

[37]  A. Pasquinelli,et al.  MicroRNA biogenesis: regulating the regulators , 2013, Critical reviews in biochemistry and molecular biology.

[38]  C. Norbury,et al.  The Long and Short of MicroRNA , 2013, Cell.

[39]  P. Visscher,et al.  The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling , 2010, PLoS genetics.

[40]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[41]  Mariarosaria Taddeo,et al.  The ethics of algorithms: Mapping the debate , 2016, Big Data Soc..

[42]  William Stafford Noble,et al.  Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.

[43]  Mingyu Liang,et al.  Ushering Hypertension Into a New Era of Precision Medicine. , 2016, JAMA.

[44]  Robert Koprowski,et al.  Machine learning, medical diagnosis, and biomedical engineering research - commentary , 2014, BioMedical Engineering OnLine.

[45]  Michael J. Brammer,et al.  The role of machine learning in neuroimaging for drug discovery and development , 2015, Psychopharmacology.

[46]  Kevin Waugh,et al.  DeepStack: Expert-level artificial intelligence in heads-up no-limit poker , 2017, Science.

[47]  Mariarosaria Taddeo,et al.  Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach , 2016, Sci. Eng. Ethics.

[48]  Elizabeth Gibney,et al.  Google AI algorithm masters ancient game of Go , 2016, Nature.

[49]  R. Touyz,et al.  Redox Stress Defines the Small Artery Vasculopathy of Hypertension: How Do We Bridge the Bench-to-Bedside Gap? , 2017, Circulation research.

[50]  Ryan Calo,et al.  There is a blind spot in AI research , 2016, Nature.