Pharmaceuticals & Biotechnology


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Silence Therapeutics' chief executive officer Ali Mortazavi has been appointed non-executive chairman of ultromics - an artificial intelligence-focused medical imaging firm. Mortazavi has 17 years' experience in UK companies, particularly in the biotechnology investment field and joined London-based biotech Silence Therapeutics in 2012 leading its refinancing business and driving business. He said: "I am truly excited to be joining the board of ultromics. Ultromics plans to use the combination of man plus machine rather than man vs Machine in medical imaging."


How AI Is Crunching Big Data To Improve Healthcare Outcomes

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Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations. By utilizing deep learning algorithms and software, healthcare providers can connect various libraries of medical information and scan databases of medical records, spotting patterns that lead to more accurate detection and greater breadth of efficiency in medical diagnosis and research. IBM Watson, which has previously been used to help identify genetic markers and develop drugs, is applying its neural learning networks to help doctors correctly diagnose heart abnormalities from medical imaging tests. Powered by Baidu's deep learning and natural language processing networks, Melody improves her communication and diagnostic skills by learning from conversations with Baidu's hundreds of millions of users.


These Non-Tech Companies Are Investing In An AI Future

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Domino's Pizza, Inc. (NYSE: DPZ), which has so heavily invested in innovation many consider it a tech company that sells pizza rather than a pizza company with technology, currently uses AI in its mobile ordering platform and is testing delivery robots. Pharmaceutical GlaxoSmithKline plc (ADR) (NYSE: GSK) is testing technology for automated drug design to hasten research, while IDEXX Laboratories, Inc. (NASDAQ: IDXX) produces animal health diagnostic tools using machine learning. AI guides the stock picks of BlackRock, Inc. (NYSE: BLK) and decreases asset management costs for Northern Trust Corporation (NASDAQ: NTRS), and Nasdaq Inc (NASDAQ: NDAQ) uses machine learning for its data analytics and market surveillance products. Similarly, Accenture plc (NYSE: ACN) informs its consulting practice with AI research, and Interpublic Group of Companies Inc (NYSE: IPG) uses machine learning to guide emotionally influential advertising.


The role of AI in the future of health care

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Out of 218 health care AI startups selected from an industry database, 54 were involved in predictive medicine, with 44 founded in 2010 or later. Out of 218 AI health care startups, 21 develop wellness applications. Founding data for AI startups helps to identify the uptick in launching startups working with cell and genetic research. Following Mayo's vision, health care researchers and founders try to make life longer by battling aging and making rehabilitation smoother.


Mayo Clinic startup uses AI to discover new medicines - Pharmaphorum

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Launched in partnership with American tech company nference, Qrativ (pronounced'curative') combines Mayo Clinic's medical expertise and clinical data with nference's AI platform nferX. The deep learning-driven AI sifts through masses of medical literature and clinical data to uncover insights into disease and will form the basis of Qrativ's Darwin.ai These insights can then be used to guide the development of new drugs. The hope for many is that the data mining capabilities of AI technology can reveal insights from clinical data that would otherwise be impossible for human researchers to do within their lifetime. For the Mayo Clinic, Qrativ continues its interest in AI.


When AI Meets Biotech, the Results Are Amazing - and Profitable

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Meanwhile, I've turned up a small British company that's using its artificial intelligence platform discover promising small molecule treatments faster – and cheaper – than ever before. According to a recent report from the Tufts Center for the Study of Drug Development, here's how the costs break down: $1.4 billion in direct spending, $1.2 billion in lost use of funds over the decade, and more than $300 million in post-approval costs. Like I said before, this is a privately held firm, so Wall Street will tell you that there's no way you can directly profit from this tiny firm's work. Today he is the editor of the monthly tech investing newsletter Nova-X Report as well as Radical Technology Profits, where he covers truly radical technologies – ones that have the power to sweep across the globe and change the very fabric of our lives – and profit opportunities they give rise to.


Study pinpoints genetic marker that makes dogs social

Daily Mail

A new study has identified a genetic marker in dogs that sets them apart from wolves when it comes to human interaction, suggesting dogs developed a genetic condition through domestication that causes them to be so sociable. A new study has identified a genetic marker in dogs that sets them apart from wolves when it comes to human interaction, suggesting dogs may have developed a genetic condition through domestication that causes them to be so sociable. The findings challenge previous research that suggests dogs were domesticated twice by separate groups living in east and western Eurasia, instead revealing all modern dogs descended from animals that were domesticated by people living in Eurasia 20,000-40,000 years ago. In the study led by Princeton University biologist Bridgett von Holdt and researchers at Oregon State University, the team put 18 domesticated dogs and 10 captive human-socialized wolves to the test using problem-solving tasks.


Atul Gawande on Priorities, Big and Small – Conversations with Tyler – Medium

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TYLER COWEN: I'm here up in Boston with Atul Gawande, and we're going to talk about health, healthcare, healthcare policy, and Atul Gawande himself. GAWANDE: OK, the diagnosis process--people imagine what it is, is that people come to you with a crisply defined problem. GAWANDE: There are plenty of reasons to be worried about CRISPR in my mind. For example, CRISPR enables gene editing that basically is fairly fixed.


A New Frontier for AI: Helping Scientists Develop Potential New Medicines

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But Austin Huang, Associate Director and the Biomedical Data Science lead in Pfizer's Genome Sciences and Technologies group in Kendall Square, Cambridge, Massachusetts, explains that "the methods that companies like Google and Facebook use to study large, complex datasets can also be used to help predict disease and possible treatment outcomes in human health data." If the ultimate goal of a self-driving car is to navigate a busy city street, in pharmaceutical research, the goal is to navigate the connections between a potential treatment and its effectiveness in treating a disease. Austin Huang, Associate Director and the Biomedical Data Science lead in Pfizer's Genome Sciences and Technologies group And if other fields of AI are any indication, he says, "when breakthroughs happen, change can follow very quickly," likening it to a "tipping point." To enable AI to reach those kinds of breakthroughs, it's important to teach computers how to "think" abstractly in discovering patterns in large datasets.


#Artificialintelligence can predict the success of IVF embryos better than do...

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Science Daily explores the issue in more depth (4 July 2017): "However, because the artificial intelligence system is a technique which analyses the embryo through mathematical variables, it offers low subjectivity and high repeatability, making embryo classification more consistent. "Nevertheless," said Professor Rocha, "the artificial intelligence system must be based on learning from a human being -- that is, the experienced embryologists who set the standards of assessment to train the system."" See also EurekAlert (4 July 2017): "The system utilizes a sophisticated architecture of multi-class deep neural networks (DNNs) and DNN ensembles trained on thousands of samples of carefully selected cells of multiple classes: embryonic stem cells, induced pluripotent stem cells, progenitor stem cells, adult stem cells and adult cells to recognize the class and embryonic state of the sample, achieving high accuracy in simulations. The sample sets were augmented with carefully selected and manually curated data from public repositories coming from multiple experiments and generated on different platforms.