Posted 15th October 2020 by Nicholas Noakes
Ahead of the Global Pharma R&D AI, Data Science and Informatics Summit we sat down with Claire Biot to talk about her work to provide life science companies & professionals with a scientific and business platform to imagine sustainable innovations, capable of improving patient & physician experience in the age of precision medicine.
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 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:
Big Data Analytics and Artificial Intelligence: 7 Design Principles to Empower Life Science Researchers
Posted 5th January 2018 by Jane Williams
Life sciences research is increasingly relying on big data analytics to drive scientific discovery. One of the contributing factors to this trend is the fact that researchers are confronted with a rapidly growing body of scientific literature and databases required to interpret experimental outcomes.
Posted 22nd December 2017 by Jane Williams
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?
Posted 4th December 2017 by Jane Williams
Recently Baker and Lithgow (Baker, 2016; Lithgow, et al., 2017) highlighted the problem of the reproducibility in research. Reproducibility criticality affects to different extent a large portion of the science fields (Baker, 2016). Bioinformatics is becoming a key element of many biological/medical studies (Searls, 2010) and reproducibility is also an important issue in this field (Kanwal, et al., 2017; Sandve, et al., 2013).
Posted 24th November 2017 by Jane Williams
Therapeutic interventions have traditionally been developed for patients with established pathologies and overt clinical symptoms. Many diseases however, are not tractable in later stages and therefore therapies are needed that intervene earlier in the disease continuum.