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Mobile Robots Propel Research with Automated Robotic Lab Assistants

#artificialintelligence

Tasks in the pharmaceutical, life sciences and biomedical industries have always been time-consuming and complex. With the advent of the Covid-19 pandemic, these undertakings will only grow in complexity. To ensure speed, accuracy and mitigate the infectivity stress among the humans, robots are called upon to meet the ever-increasing range of workflows in today's research and development laboratories. Laboratory automation, drug discovery and pharmaceutical manufacturing are emerging fields where the services of robots are leveraged for research and development. Robotic lab assistants help researchers and scientists focus on high-level tasks like the analysis of potential therapeutic compounds rather than mundanely mixing compounds to determine their curative characteristics.


AI: the smart money is on the smart thinking - PMLiVE

#artificialintelligence

AI could also have a transformative effect on clinical decision-making through the utilisation of the huge levels of genomic, biomarker, phenotype, behavioural, biographical and clinical data that is generated across the health system. Bayer and Merck & Co provide a perfect example of this. They have developed an AI software system to support clinical decision-making of chronic thromboembolic pulmonary hypertension (CTEPH) – a rare form of pulmonary hypertension. The software helps differentiate patients from those suffering with similar symptoms that are actually a result of asthma and chronic obstructive pulmonary disease (COPD), and therefore diagnose CTEPH more reliably and efficiently. The CTEPH Pattern Recognition Artificial Intelligence obtained FDA Breakthrough Device Designation in December 2018.


ZS's 2020 state of AI in Life Sciences

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ZS surveyed 110 life sciences executives to gain a better understanding of AI adoption across the industry. Explore the top three uses of AI in life sciences, the industry's biggest barriers, and where many believe AI will have the biggest impact over the next 5 years.


Bridging artificial intelligence and life sciences – launch of ELLIS Heidelberg – Tech Check News

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A newly established research unit will support artificial intelligence and machine learning in the life sciences, and will link its activities in Heidelberg with other research institutes internationally. The unit is part of ELLIS – the European Laboratory for Learning and Intelligent Systems – and will enable substantial progress in data analysis in medicine and the life sciences. ELLIS Heidelberg was founded by scientists from the German Cancer Research Center, EMBL Heidelberg, and Heidelberg University.


Are Clogged Blood Vessels the Key to Treating Alzheimer's Disease?

Discover - Top Stories

Citizen Science Salon is a partnership between Discover and SciStarter.org. In 2016, a team of Alzheimer's disease researchers at Cornell University hit a dead end. The scientists were studying mice, looking for links between Alzheimer's and blood flow changes in the brain. For years, scientists have known that reduced blood flow in the brain is a symptom of Alzheimer's disease. More recent research has also shown that this reduced blood flow can be caused by clogged blood vessels -- or "stalls." And by reversing these stalls in mice, scientists were able to restore their memory.


Challenge Yourself by Reaching for the Highest Bar

Communications of the ACM

Challenge yourself and reach for the highest bar. If you succeed, keep pushing the boundaries." This is what my friend Hassan Hajji advised when I started my career at IBM Research Tokyo in 2002, and these words have been a guiding force in my career ever since. At IBM, I was challenged to learn as much as possible about the research process in an industrial lab (prototyping ideas, patenting, publishing results), and it dovetailed nicely with my desire to work toward a Ph.D. in systems biology. After receiving my doctorate, which allowed me to enhance my skills in computational and mathematical analysis to understand complex biological systems, I was ready for a new challenge. I left Japan to work in the U.K. at a small startup, ecrebo,a which provides a coupon-issuing system for retailers who seek to attract customers based on their individual purchasing habits. I was responsible for developing a backend server for the coupon system. It had to be able to analyze the contents of the receipt, determine whether it met the conditions for issuing the coupon, and return it within three seconds, including communication time with the POS system.


Apprentice raises $7.5 million to expedite lab work with AI and AR

#artificialintelligence

Apprentice.io, a startup developing a conversational AI and augmented reality platform for pharmaceutical, biotech, and chemical companies, today announced it has raised $7.5 million. CEO and cofounder Angelo Stracquatanio says the capital will enable Apprentice to scale to accommodate customer growth attributable to the pandemic. A shortage of lab workers is hastening the adoption of automation-driven "augmentation" technologies. An American Society for Clinical Pathology study revealed that the increasing workload is compelling lab managers to hire recent graduates or candidates with bachelor's degrees but no laboratory training. Automation and digital guidance tools like Apprentice's can upskill young professionals while ensuring quality standards aren't compromised.


5 pitfalls AI healthcare start-ups need to avoid -

#artificialintelligence

Artificial intelligence (AI) has now moved beyond its initial hype towards becoming a key part of the pharma industry – with many companies looking to partner with AI drug discovery start-ups. Pharma and healthcare are data-rich industries and AI helps by turning data into actionable insights, allowing us to solve complex, intricate problems. Using machine learning, AI algorithms can generate patterns that will enable us to predict toxicity, find potential combination treatments, identify and predict new drugs and expand usage of current drugs in other diseases. However, only a handful of companies from the swarm of AI start-ups have successfully gained traction within the pharma industry. Over the past month, I've had conversations with several growing AI drug discovery companies and have analysed some critical strategic shortcomings that can frustrate the upwards journey of these start-ups: AI cannot be built in isolation without understanding the nuances and the complexity of the business needs it will address.


Artificial-intelligence tools aim to tame the coronavirus literature

Nature

New AI technologies are helping scientists to sort through the wealth of COVID-19 papers -- hopefully hastening the research process.Credit: Adapted from Getty The COVID-19 literature has grown in much the same way as the disease's transmission: exponentially. But a fast-growing set of artificial-intelligence (AI) tools might help researchers and clinicians to quickly sift through the literature. Driven by a combination of factors -- including the availability of a large collection of relevant papers, advances in natural-language processing (NLP) technology and the urgency of the pandemic itself -- these tools use AI to find the studies that are most relevant to the user, and in some cases to extract specific findings from the results. Beyond the current pandemic, such tools could help to bridge fields by making it easier to identify solutions from other disciplines, says Amalie Trewartha, one of the team leads for the literature-search tool COVIDScholar, at the Lawrence Berkeley National Laboratory in Berkeley, California. The tools are still in development, and their utility is largely unproven.


How The Cloud Can Solve Life Science's Big Data Problem

#artificialintelligence

Small startups and big companies alike are recognizing that modern biotech R&D is as much a data ... [ ] problem as a science problem. Cloud technologies offer a way to bring together massive amounts of complex data to improve the way we feed, fuel, heal, and build our world with biology. These days, biotech R&D is as much a data problem as a science problem. Here's why: in the past decade, the exploding field of synthetic biology has done an incredible job solving the scientific challenges of making biology easier to engineer. I have written about how tools like gene editing, synthesis, sequencing, and automation are changing for the better the way we feed, fuel, heal, and build our world with biology.