Innovations In Clinical Neuroscience

Hot Topics in Drug Development Apr 2016

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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ORDER FROM CHAOS— WHERE BIG DATA AND ANALYTICS ARE HEADING, AND HOW LIFE SCIENCES CAN PREPARE FOR THE TRANSFORMATIONAL TIDAL WAVE In this article from EY, the authors offer an excellent overview of the five major trends we're seeing in life sciences— Disruptive consumer technology, personalized medicine, analytics, maturing capabilities of cloud computing, and healthcare cost reductions—and how these trends can lead to more patient-centric medicine. The authors ask "Patient- centricity and enterprise transformation powered by big data-driven advanced analytics is a reality. Is your organization ready?" * Read more here: EY-OrderFromChaos/$FILE/EY- OrderFromChaos.pdf HOW GOOGLE'S USING BIG DATA AND MACHINE-LEARNING TO AID DRUG DISCOVERY Here, Paul Sawyers from VentureBeat briefly explains how the internet giant Google is working to expedite the discovery of drugs through their deep learning model. * Read more here: googles-using-big-data-and-machine- learning-to-discover-new-drugs EVERYTHING YOU NEED TO KNOW ABOUT DEEP LEARNING AND NEURAL NETWORKS "Often coined machine learning or neural networking, deep learning involves 'training' a computational model so it can decipher natural language..." Have no idea what deep learning is? This article in Techworld by Margi Murphy explains it in a straight-forward, reader-friendly way. * Read more here: does-google-need-deep-neural-network- deep-learning-3623340/ GOOGLE'S NEXT BIG IDEA: MINING DATA TO PREVENT DISEASE In an article published by STAT, Charles Piller interviews Dr. Jessica Mega, who left Harvard Medical School to become medical director of Google Life Sciences. In the interview, Dr. Mega describes the groundbreaking clinical trials that used genetic and molecular signals of disease to study customized treatments for heart patients. She is now leading Google's life sciences initiative to analyze genomic, molecular, and imaging big data from 10,000 volunteers "to figure out what it means to be healthy—the so-called baseline study." * Read more here: e-doctor-jessica-mega/ FOUR COMPANIES USING DEEP LEARNING FOR DRUG DISCOVERY This article posted on describes how the companies TwoXAR, Atomwise, Insilico Medicine, and Berg Health are using deep learning technology to discover new drug compounds. A prediction of their success is provided. * Read more here: companies-using-deep-learning-for-drug- discovery/ ARTIFICIAL INTELLIGENCE VS. DEEP LEARNING VS. BIG DATA Confused by all the new technology nomenclature? Newbies in drug development should definitely familiarize themselves with the terms defined in this article, because you'll be hearing and reading a lot about them in the media and at medical conferences. In this article * Read more here: ial-intelligence-vs-deep-learning-vs-big- data/ DATA SHARING: AN OPEN MIND ON OPEN DATA The move to make scientific findings transparent can be a major boon to research, but it can be tricky to embrace the change. This article by Virginia Gewin in Nature does a great job describing the data sharing movement and what it means to junior researchers. * Read more here: 529/n7584/full/nj7584-117a.html GENTLE INTRODUCTION TO PREDICTIVE MODELING Just as the title of this article suggests, Jason Brownlee provides a beautifully simple step-by-step introduction to predictive modeling--a concept that is all part of deep learning/big data movement in drug development. * Read more here: tle-introduction-to-predictive-modeling/ Hot Topics in Drug Development [April 2016] 6

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