Posted 26th November 2018 by Jane Williams
The promise of an effective set of tools based on deep learning or other machine learning algorithms is the current buzz of the digital pathology markets. While the evolving tools, models and techniques are producing strongly positive results, there are still many factors which impact the utility and portability of models and tools being created across real-world data sets.
Human Genomic Evidence: Revolutionising the identification and prioritisation of targets for better medicines
Posted 17th October 2018 by Jane Williams
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.
Posted 28th September 2018 by Jane Williams
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.
Posted 17th September 2018 by Jane Williams
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.
Posted 3rd September 2018 by Jane Williams
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:
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.
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.