life sciences

nference Scores $11M to Strengthen AI for Life Sciences


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


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.

AI in the Life Sciences: Six Applications GEN


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.

WekaIO CEO says focus will stay on AI, life sciences


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


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


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 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.

Opentrons releases next-gen robot in bid to become the "PC" of biology labs


If you've ever worked in a life sciences lab, there's a good chance you hate one task more than any other: Pipetting liquid in small quantities with no margin for error. Automated pipetting solutions exist, but up to 90 percent of scientists around the world still run experiments manually because the cost and complexity of lab machines is prohibitive. Following a trend we're seeing with cheap table-top 3D printers, which are fast bringing advanced manufacturing to the masses, a company called Opentrons just announced a new version of its small pipetting robot, which costs about $4000, far less than other automated lab solutions. The advances in 3D printers have driven the costs of precision automation down significantly, in large part paving the way for spinoff technologies like this. Opentrons says its hoping to become the PC of biology labs.

Second annual Women in Data Science conference showcases research, explores challenges

MIT News

Two hundred students, industry professionals, and academic leaders convened at the Microsoft NERD Center in Cambridge, Massachusetts for the second annual Women in Data Science (WiDS) conference on March 5. The conference grew from 150 participants last year, and highlighted local strength in academics and health care. "The WiDS conference highlighted female leadership in data science in the Boston area," said Caroline Uhler, a member of the WiDS steering committee who is an IDSS core faculty member and assistant professor of electrical engineering and computer science (EECS) at MIT. "This event is particularly important to encourage more female scientists in related areas to join this emerging area that has such broad societal impact." Regina Barzilay, Delta Electronics Professor of EECS, gave the first presentation on how data science and machine learning approaches are improving cancer research. Barzilay said her experiences as a breast cancer survivor motivates her work.

IIoT, Digital Transformation, Smart Manufacturing, Life Sciences


The Life Sciences industry is leveraging IIoT, digital transformation, and smart manufacturing to address some of its primary manufacturing concerns, including improving productivity and reducing costs, the ability to document and secure everything from raw materials to process changes and software version control, and serialization and track and trace capabilities to meet local regulations while ensuring the highest levels of security. The ability to move from batch processes to continuous processes, particularly for bulk pharmaceuticals, is critical, especially with the use of disposable and modular production increasing. As with all industries in an IIoT connected world, cybersecurity continues to be a significant issue and an area that requires further investments. There are a number of market trends shaping the life sciences industry. Global population and life expectancies are increasing.