Since research in synthetic biology began nearly two decades ago, the field has expanded beyond its original mandate of using engineering principles to study and manipulate cells. Today, scientists are building biological computers and DNA-based robots that can carry out logical operations and complete tasks. These miniscule machines look nothing like laptops or Roombas. Yet, algorithms still guide the robots through tasks, and the biological computers funnel inputs through logic gates. While a standard circuit works with electrical currents, though, the inputs in the biological version are biochemical signals triggered by presence of a protein or pathogen.
How do you see the current landscape for digital and data science in pharma? The timescale under which we operate today is not the fastest. I bet two years from now the same conversations will probably be going on, just with different faces trying to make the same impact in different companies. You have to ask yourself how much of what we're doing right now is truly impactful vs trying to marginally improve an already inefficient process. What's been even scarier is that we're looking at digital being the utopian cure for everything when I actually think it's the reverse – it's becoming something like an anti-bacterial agent that's developing its own resistance and pitfalls, and I think the companies that are going to win in this space with customers are the ones working on the antidote and scale.
From building the Deep Blue computer that beat Garry Kasparov at chess to the Watson artificial intelligence (A.I.) that won Jeopardy, IBM has been responsible for some high-profile public demonstrations of A.I. in action. Its latest showcase is less high concept, but potentially far more transformative -- applying machine learning technology to the subject of organic chemistry. As described in a new research paper, the A.I. chemist is able to predict chemical reactions in a way that could be incredibly important for fields like drug discovery. To do this, it uses a highly detailed data set of knowledge on 395,496 different reactions taken from thousands of research papers published over the years. Teo Laino, one of the researchers on the project from IBM Research in Zurich, told Digital Trends that it is a great example of how A.I. can draw upon large quantities of knowledge that would be astonishingly difficult for a human to master -- particularly when it needs to be updated all the time.
Dr. Geraci's efforts mainly concern precision medicine, using mathematical and computational methods to construct models of disease that go beyond classical top-down clinical definitions. After completing postdocs in oncology, biological psychiatry, and artificial intelligence he created NetraMark Corp where he has been developing novel technologies that aid in the understanding of our molecular and brain circuitry in addition to novel machine learning algorithms specialized to help understand complex patient populations. He is also a professor of Molecular Medicine at Queen's University in Ontario. Dr. Geraci has a strong interest in advancing the mathematical methods being employed in the study of our molecular circuitry (protein, microRNA, mRNA), the analysis of brain MRIs, and machine learning that can use variables that are beginning to emerge due to our interaction with technologies like fitness watches and smart buildings. A major interest of his is an ongoing project involving translating the vast amount of genetic and proteomic patient data, coupled with our current knowledge of our molecular circuitry, into a scoring scheme that can reveal potential new drug targets.
Medicine is ripe for disruption. The costs that result from poor quality trickle down to consumers and patients, who shoulder much of the burden of ever-increasing healthcare costs. In order to improve healthcare accessibility, the utilization of medical resources must be made more accurate, more efficient, and more secure. These technologies are blockchain and artificial intelligence. By utilizing the latest advancements in these technologies, the medical industry can improve quality, bring down cost, and democratize healthcare like never before.
SAN FRANCISCO--(BUSINESS WIRE)--The following is an opinion editorial provided by Navin Shenoy, executive vice president and general manager of the Data Center Group at Intel Corporation. In the wide world of big data, artificial intelligence (AI) holds transformational promise. Everything from manufacturing to transportation to retail to education will be improved through its application. But nowhere is that potential more profound than in healthcare, where every one of us has a stake. What if we could predict the next big disease epidemic, and stop it before it kills?
Machine learning, the most fundamental form of artificial intelligence, has started infiltrating the medical field, and it seems machines can play a crucial role in improving our health. A study of over 50 executives in the healtcare sector by TechEmergence revealed that by 2025 AI will be adopted on a broader scale. If there's one thing the healthcare industry has in abundance, it's undoubtedly data. And machine learning algorithms work better if they are exposed to more data. The savings would also be huge.
The United States spends a larger percentage of its gross domestic product on healthcare than other wealthy countries, yet the nation's health outcomes are worse. And while that is an oft-cited reality, almost half of the country's expenditures are tied up in administrative costs -- which could potentially be addressed with artificial intelligence. AI technology is already in use in certain areas of healthcare, such as diagnostic imaging. Yet the authors of a new study published in the Journal of the American Medical Association contend it's being underutilized thus far. One of the big opportunities in which AI could help is population health because it is a tricky endeavor.
BenevolentAI, a UK company using artificial intelligence for drug development, has raised $115 million in new funding, mostly from undisclosed investors in the United States. Existing backer Woodford Investment Management also participated in the round, which brings the company's total funds raised to over $200 million. "We are very pleased with the response to the fundraising," Ken Mulvany, founder and chairman of BenevolentAI, said in a statement. "It reflects the rapidly growing global interest in the AI pharmaceutical sector and the recognition of our place as the dominant player within it. We have come a very long way since we founded the business in 2013.