Pharma R&D Today

Ideas and Insight supporting all stages of Drug Discovery & Development

Select category
Search this blog

Ontologies – what do they have to do with pharma research?

Posted on August 24th, 2022 by in AI & Data

You would be forgiven for thinking that the topic of ontology is more the domain of philosophers or linguists – and not necessarily the concern of someone working in the life sciences. After all, the first definition you get if you Google the word ontology is “the branch of metaphysics dealing with the nature of being”. But dive deeper into the term, and you’ll discover that it’s about classifications of things and their relationships to each other – and that can most certainly be of interest to scientists.

(more…)

Umesh Nandal: Chemist and data scientist in one

Posted on March 10th, 2022 by in AI & Data

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: curing disease,” says Umesh.

(more…)

4 Notable Life Sciences Trends from the Tech Trends Report

Posted on June 4th, 2021 by in AI & Data

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 those in the pharmaceutical and life sciences sectors.

(more…)

Mutually empowering – semantic-based machine learning and subject matter expertise

Posted on May 7th, 2021 by in AI & Data

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, drive language translation, take a crack at gaining insights from social media platforms, and generate answers to questions faster. His most important take-away: semantic-based ML and subject matter expertise (SME) are mutually empowering.

(more…)

  1. 1
  2. 2
  3. 3
  4. 7