Proteins are the building blocks for all living things, providing structure and managing processes in cells. Understanding how these molecules fold into specific 3D shapes is key to understanding their function but requires expensive equipment and lots of time, limiting the progress of research and development. A new artificial intelligence programme called AlphaFold has been shown to accurately predict protein structure in minutes, solving a decades old challenge. Its success is built on the availability of thousands of experimentally determined protein structures, a result of long-term research funding, infrastructure investment and data-sharing policies. DeepMind, the developers of AlphaFold, have made the AlphaFold code and protein structure predictions openly available to the global scientific community.
Deloitte today announced the launch of its ConvergeHEALTH CognitiveSpark for Marketing artificial intelligence (AI) precision engagement solution, a module of the CognitiveSpark suite. CognitiveSpark for Marketing harnesses the power of AI to boost digital marketing return on investment (ROI) for life sciences companies, helping marketers make AI-powered decisions at scale and with speed. CognitiveSpark for Marketing harnesses the power of AI to boost digital marketing ROI for life sciences companies. A recent Deloitte survey of biopharma executives found that digital innovation is now a burning priority, with 77% of those surveyed saying their organization considers "digital innovation as a competitive differentiator." And in the same survey, 86% of commercial leaders pointed to "health care provider (HCP)/patient engagement as the top use case likely to be impacted by digital innovation."
At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I was working on exotic niche problems. Well, the field has matured substantially and here I propose to have a look at the top applications of GNNs that we have recently had. If this in-depth educational content on graph neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material.
At the point when the human genome project prevailed with regards to planning the DNA succession of the human genome, the international research community was energized by the chance to all more likely comprehend the genetic instructions that impact human wellbeing and advancement. DNA conveys the genetic data that decides everything from eye tone to defencelessness to certain sicknesses and issues. The around 20,000 segments of DNA in the human body known as qualities contain directions about the amino acid sequence of proteins, which play out various fundamental capacities in our cells. However, these qualities make up under 2% of the genome. The leftover base sets -- which represent 98% of the 3 billion "letters" in the genome -- are designated "non-coding" and contain less surely knew guidelines concerning when and where qualities ought to be created or communicated in the human body.
Amazon is the Standard Oil of the 21st century. Its business operations and global reach dwarf those of virtually every other company on the planet -- and exceed the GDP of more than a few countries -- illustrating the vital importance innovation has on the modern economy. In his latest book, The Exponential Age: How Accelerating Technology is Transforming Business, Politics and Society, author Azeem Azhar examines how the ever-increasing pace of technological progress is impacting, influencing -- and often rebuilding -- our social, political and economic mores from the ground up. Excerpted from The Exponential Age: How Accelerating Technology is Transforming Business, Politics and Society by Azeem Azhar. In 2020, Amazon turned twenty-six years old.
Bringing new medicines to today's market can be … complex and complicated. In the biopharmaceutical industry, it can take up to 15 years to go from an idea for a drug to a medicine that's approved for patients--a process that requires (and produces) a staggering amount of data. Even for a global biopharmaceutical leader such as AstraZeneca, research and development have traditionally been lengthy endeavors plagued by arduously manual processes. And because of recent advances in understanding the human genome and how it intersects with disease biology, the company faces an influx of data as it looks to identify new drug targets and develop next-generation therapeutics. Back in 2019, Anna Berg Åsberg, Global Vice President of R&D IT, began conversations with R&D leaders about where her department could apply technology to automate and augment R&D processes as well as help make the best decisions on which research projects to advance through the pipeline.
WIRE)--Alation Inc., the leader in enterprise data intelligence solutions, today announced the acquisition of Lyngo Analytics, a Los Altos, Calif.-based data insights company. The acquisition will elevate the business user experience within the data catalog, scale data intelligence, and help organizations drive data culture. Lyngo Analytics CEO and co-founder Jennifer Wu and CTO and co-founder Joachim Rahmfeld will join the company. Lyngo Analytics uses a natural language interface to empower users to discover data and insights by asking questions using simple, familiar business terms. Alation offers the most intelligent and user-friendly machine-learning data catalog on the market.
What if a company built each component of its product from scratch with every order, without any standardized or consistent parts, processes, and quality-assurance protocols? Chances are that any CEO would view such an approach as a major red flag preventing economies of scale and introducing unacceptable levels of risk--and would seek to address it immediately. Yet every day this is how many organizations approach the development and management of artificial intelligence (AI) and analytics in general, putting themselves at a tremendous competitive disadvantage. Significant risk and inefficiencies are introduced as teams scattered across an enterprise regularly start efforts from the ground up, working manually without enterprise mechanisms for effectively and consistently deploying and monitoring the performance of live AI models. Ultimately, for AI to make a sizable contribution to a company's bottom line, organizations must scale the technology across the organization, infusing it in core business processes, workflows, and customer journeys to optimize decision making and operations daily.
A new study identifies genes that enable plants to grow with less fertilizer. Machine learning, a type of artificial intelligence used to detect patterns in data, can pinpoint "genes of importance" that help crops grow with less fertilizer, according to a U.S. National Science Foundation-funded study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. "This is an excellent example of how NSF-supported scientists lead the way in using AI and cutting-edge computational approaches to accelerate translation of basic plant genomic research and discoveries to the field," said Diane Okamuro, a program director in NSF's Division of Integrative Organismal Systems. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology.
The impact of climate change on Brazil's Atlantic coastline is a research focus at the University of São Paulo's machine-intelligence centre.Credit: Antonello Veneri/AFP via Getty Artificial intelligence (AI) is increasingly becoming a tool for researchers in other science and technology fields, forging collaborations across disciplines. Stanford University in California, which produces an index that tracks AI-related data, finds in its 2021 report that the number of AI journal publications grew by 34.5% from 2019 to 2020; up from 19.6% between 2018 and 2019 (see go.nature.com/3mdt2yq). AI publications represented 3.8% of all peer-reviewed scientific publications worldwide in 2019, up from 1.3% in 2011. Five AI researchers describe the fruits of these collaborations, beyond journal publications, and talk about how they are helping to break down barriers between disciplines. At the University of São Paulo in Brazil, where I lead the Center for Artificial Intelligence (C4AI), our main goal is to produce machine-intelligence research that has a direct impact on society and industry.