Category: AI & Data
There are life-changing insights waiting to be found in chemistry data. The critical information waiting to be unlocked can lead to incredible technology advances and medical breakthroughs. But there is so much data out there, and it is multiplying rapidly. At Elsevier, we are excited about a new partnership with world-renowned innovator LG that we […]
Since its initial release in January 2021, AI-fueled Patent Expansion in Reaxys has won the respect of pharmaceutical companies trying to stay ahead of their competitors by keeping track of the latest patents around drug discovery and new therapeutics. With a recent expansion that allows Reaxys to also loop in information from images found in patents, it’s […]
Network In recognition of her numerous research accomplishments, Professor Karin Verspoor is nominated for the prestigious Women in AI Award under the ‘AI in Innovation’ category. Certainly, the interdisciplinary work she has undertaken with Elsevier has chemists and pharmaceutical companies excited about the real-world impact of automating the extraction of chemical reaction information from patents. […]
Umesh Nandal is a Director of Data Science at Elsevier. As an AI expert with a Master’s in Chemistry, he embodies what Elsevier brings to the table – an actionable fusion of data and domain expertise. “Cross-functional teams combining data science, tech and domain knowledge is the only way we can achieve our collective goal: […]
Mark Sheehan is VP Data Science on Elsevier’s Life Science team. His 20 years at the company maps closely with Elsevier’s digital journey over that time. And today, pharmaceutical companies can follow a similar journey – albeit highly accelerated – using Elsevier’s latest AI-driven R&D boosters. Mark has enjoyed many valuable experiences along the way, […]
Your AI applications are only as strong as your data. This is why Elsevier strives to make data not only align with FAIR usability principles but also be maximized for trustworthiness and relevance. In this way we can confidently offer the full potential of data science in an easy-to-use manner – from data acquisition and […]
If you work in Life Sciences & Health, you’re likely aware of what Data Science and AI can bring to the table when it comes to sparking innovation. However, if you are not Big Pharma, it’s the how that slows down most LSH companies and organizations. We believe that Elsevier’s road-tested data and semantic technologies can address […]
AI and precision medicine are both changing drug discovery and development as we know it. Precision medicine that is powered by AI stands to be transformative, especially for the treatment of rare diseases. Elsevier was recently part of a collaboration that sought to demonstrate how AI precision medicine could lead researchers to new treatments for […]
In most aspects of life, we think of “endless possibilities” as a good thing. But in science, the wealth of possibilities can be daunting. Pharmaceutical researchers typically start with a disease that they want to tackle, find a target, and then try to identify compounds which interact with that target. There may be around 200-300 […]
When thinking of artificial intelligence (AI), many people rely on cultural touchstones from science fiction films and TV. They might think of HAL, the calm-voiced AI from 2001: A Space Odyssey, or Data, the even-tempered android from Star Trek. We tend to imagine that artificial intelligence will be driven by reason, logic and facts – […]
The Future Today Institute has released its 2021 Tech Trends Report (14th Annual Edition), a comprehensive report on strategic trends that it anticipates will affect business, government, education, media and society in the coming year. We noted four trends discussed in the report’s Health, Medicine & Science section that will be of particular interest to […]
In a day dedicated to emerging science and technologies at the Pistoia Alliance virtual conference Collaborative R&D in Action, SciBite CTO James Malone opened the program with a compelling exploration of use cases for semantic-based machine learning (ML). A simple but elegant ML strategy based on “seeding” named entity recognition (NER) can facilitate ontology creation, […]