life sciences


How Artificial Intelligence Is Accelerating Life Sciences

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The drug development lifecycle is long and fraught with heavy risk -- it takes a staggering 10 – 15 years on average, with ultimately only 12 percent of drugs in clinical trials gaining approval by the U.S. Food and Drug Administration (FDA) [1]. To put this in perspective, 22.7 percent of all global research and development spending in 2017 was in the healthcare industry, second only to 23.1 percent spent in the computing and electronics industry, yet the product lifecycle and cost are much higher [2]. For example, the original iPhone took two and a half years to develop from concept to launch, and an estimated $150 million spent in research and development [3]. In contrast, the average cost of new drug and biologics is $2.87 billion when factoring in the post-approval research and development costs, according to figures released in May 2016 by The Tufts Center for the Study of Drug development (CSDD) [4]. For pharmaceutical companies that have launched more than four drugs, the median cost is closer to a staggering $5.3 billion according to analysis by industry expert Matthew Herper of Forbes [5].


Survey: Life Sciences lagging behind in AI development - insideHPC

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The Pistoia Alliance has released results of a survey that found that 72 per cent of science professionals believe their sector is lagging behind other industries in the development of AI. To accelerate the successful use of AI, The Pistoia Alliance has launched its Centre of Excellence for AI in Life Sciences, aiming to encourage greater collaboration between stakeholders to bridge the gap between technology and science. The aim of the centre is to bring together best practice, adoption strategy, events, and hackathons covering a range of challenges. The survey found adoption of AI is high, with 69 per cent of companies using AI, machine learning, deep learning, and chatbots; an increase from when the same question was asked in September 2017, where 44 per cent of respondents were using or experimenting with AI. This survey shows interest in AI remains strong, but there is still a challenge with moving past the hype to a reality where AI is delivering insights with the power to truly augment researchers' work," commented Dr Steve Arlington, president of The Pistoia Alliance.


nference Scores $11M to Strengthen AI for Life Sciences

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With an injection of $11 million in Series A financing, a start-up called nference is hoping to shake up the life-sciences industry through its artificial intelligence (AI) technologies. Founded in 2013, the company aims to "synthesize the exponentially growing biomedical knowledge," using neural networks to glean insights from scientific literature, genomics, and real-world evidence, according to the announcement. To date, nference has raised $14 million in funding in pursuit of that goal, according to the start-up tracker Crunchbase. "Natural language is the connective fabric across all therapeutic areas and support functions of large pharmaceutical companies," Venky Soundararajan, PhD, the organization's founder and chief scientific officer, said in a statement, adding that nference's goals would change life sciences. "This presents a paradigm shift toward hypothesis-free scientific research and AI-augmented [research and development] R&D decision making."


Medidata acquires SHYFT analytics for $195 million, adds analytics to life sciences suite

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Medidata said it will acquire SHYFT for $195 million in a deal designed to expand its reach in life sciences and clinical analytics. In a statement, the company said it already owned a 6 percent stake in SHYFT. Medidata combines CRM, research and third party data to help pharmaceutical, biotech and medical device companies commercialize and develop drug and product discoveries. SHYFT's analytics and data cloud weaves data sources together, provides visualizations and cleans and transforms data to provide insights. Life sciences and health care are ripe for everything from artificial intelligence to big data to digital transformation projects.


Statistical Reasoning for Public Health 2: Regression Methods Coursera

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Structure: Good structure and went through all the basic principles of statistics in detail. Appreciated how it did not have to go through the methodology of each method, but taught us how to appreciate it and understand the data as it was presented in the literature. I liked how John went through the examples in the literature so it was good to see how it was utilised in practice. I wish there was a separate course to teach us how to use these methods with sample data, perhaps a taster of this would have been good to include? but I do understand that would be challenging for some. I think some in-video questions would have been good to check-up on the progress of learning.


AI in the Life Sciences: Six Applications GEN

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More is better when it comes to Big Data and machine learning. This is particularly true in the fields of medicine and pharma. A report by Accenture estimates that by the year 2026, Big Data in conjunction with machine learning in medicine and pharma will be generating value at a prodigious rate: $150 billion/year. This figure reflects how the tools of artificial intelligence (AI) are expected to help doctors, patients, insurers, and overseers reach better decisions, optimize innovations, and improve research and clinical trial efficiency. Healthcare data comes from myriad sources: hospitals, doctors, patients, caregivers, and research.


Statistical Reasoning for Public Health 2: Regression Methods Coursera

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This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.


WekaIO CEO says focus will stay on AI, life sciences

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WekaIO CEO Liran Zvibel has a two-pronged plan for launching the parallel-file-system startup to success: He intends... You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.


United Kingdom Plans $1.3 Billion Artificial Intelligence Push

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The United Kingdom is planning a big investment in artificial intelligence technologies in a deal worth nearly £1 billion, or about $1.3 billion. The U.K. government said Thursday that part of its multi-year AI investment–about £300 million, or more than $400 million–would come from U.K.-based corporations and investment firms and those located outside the country. Some of the U.S.-based companies involved with the U.K.'s AI deal include Microsoft, Hewlett Packard Enterprise, IBM, McKinsey, and Pfizer, but the U.K. did not say how much each firm was planning to individually invest. A few of these U.S. companies helped consult on an earlier independent review on developing AI in the U.K. that the government is using as a template for its new initiative. Antony Phillipson, the United Kingdom's trade commissioner for North America, said that the investments are part of a broader set of initiatives the U.K. government is undertaking to address several areas U.K. lawmakers believe will soon affect the country's economy and society.


Machine Learning: Global Markets to 2022

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Report Scope: In this report, the market has been segmented based on type, deployment, organization size, end-user industries, and geography.The report covers the overview of the global market for machine learning and analyses the market trends, considering the base year of 2016 and estimates for 2017 to 2022. Revenue forecasts from 2017 to 2022 for segmentation based on deployment, organization size, end-user industries, and geography have been estimated with values derived from solutions and service providers' total revenues. The report also includes a section on the major players in the market.Further, it explains the major drivers, competitive landscape, and current trends in the machine learning market. The report concludes with an analysis of the machine learning vendor landscape and includes detailed profiles of the major players in the global machine learning market. Report Includes: - 45 data tables and 32 additional tables - An overview of the global market for machine learning - Analyses of global market trends, with data from 2016 and 2017, and projections of compound annual growth rates (CAGRs) through 2022 - Identification of segments with high growth potential and their future applications - Explanation of major drivers and regional dynamics of the market and current trends within the industry - Detailed profiles of major vendors in the market, including Amazon.com Inc., Alphabet Inc., Baidu Inc., Intel Corp. and Hewlett Packard Enterprise Company Summary Machine learning is one of the fastest growing areas of computer science, with a wide range of applications.Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.