Human Genomic Evidence: Revolutionising the identification and prioritisation of targets for better medicines
Posted 17th October 2018 by Kieran Chambers
Nine out of 10 potential drugs that enter clinical trials never make it to the market. Failure often occurs because the biological target chosen is not well understood. However, it is hard to objectively select targets with a high chance of clinical success because the data required to predict efficacy and safety are complex, dispersed and incomplete. To address this challenge, Open Targets was founded in 2014 as a public-private partnership by GSK, EMBL-EBI and the Wellcome Sanger Institute. The consortium has grown since its launch, welcoming new partners Biogen in 2016, Takeda in 2017, and Celgene in 2018.
Cutting through the hype: Brandon Allgood talks AI and machine learning
Posted 28th September 2018 by Kieran Chambers
When we last spoke, Brandon Allgood told us about The Future of AI. This week, we caught up with Brandon and spoke about drug discovery and machine learning algorithms.
Analytics and Big Data in Pharma
Posted 17th September 2018 by Kieran Chambers
Drug discovery and development is a complex process that requires integration of multiple data points, experiments and calculated risk/benefit assumptions. It is therefore only natural that virtualization and big data analytics are a natural fit for implementations, expected to demonstrate significant cost-effective gains for the betterment of society, providing access to effective and safe medications.
The Future of AI: A Q&A with Brandon Allgood
Posted 3rd September 2018 by Kieran Chambers
Brandon Allgood is the Chief Technology Officer at Numerate, as well as a co-founder. In addition to being responsible for the development of Numerate’s cloud-based machine learning platforms, Brandon runs the software & data science teams, and is the technical lead on all of the companies internal drug programs, as well as external collaborations.
Here, Brandon talks about what he thinks the future holds for AI and machine learning:
Truth and Fictions, Stances and Beliefs about Artificial Intelligence
Posted 15th August 2018 by Jane Williams
At the Digital Pathology and AI Conference in New York City, it was interesting to consider the different beliefs represented about Artificial Intelligence.
Distinction of Benign and Malignant Lesions through Deep Learning
Posted 6th August 2018 by Jane Williams
Machine learning is already prevalent in many industries and most pathologists are unaware how accessible machine learning is and how it can be used to augment their work or research. Applications include decision support, image analytics, process improvement, disease diagnosis and prognosis.
Deep Learning in Digital Pathology
Posted 2nd February 2018 by Anna Gomez
“Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is” – Geoffrey Hinton (Google)
Staying Ahead of Life Sciences Research and Implementation Trends in 2017 and Beyond
Posted 22nd December 2017 by Laura Berry
The traditional product pipeline, which information technology will compress and alter significantly in the life sciences in the coming years. Source: Wikimedia user MattBishop5 (CC BY-SA 3.0).
This article was originally published by Dassault Systèmes BIOVIA in October 2017, and is published here with permission.
Where can life science companies go operationally to maintain or create a competitive advantage? This question has been a constant in the industry since its inception, but recent advances in older technologies like Laboratory Information Management Systems (LIMS) and the appearance of promising newer technologies like the Internet of Things (IoT) have changed the nature of the answer. Which new technologies that are theoretically worthwhile have matured enough to be gracefully implemented operationally?
