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Tag: machine learning

Analytics and Big Data in Pharma

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.

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The Future of AI: A Q&A with Brandon Allgood

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:

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Truth and Fictions, Stances and Beliefs about Artificial Intelligence

At the Digital Pathology and AI Conference in New York City, it was interesting to consider the different beliefs represented about Artificial Intelligence.

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Distinction of Benign and Malignant Lesions through Deep Learning

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.

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Deep Learning in Digital Pathology

“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)

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Staying Ahead of Life Sciences Research and Implementation Trends in 2017 and Beyond

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?

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Big data, knowing and AI: do they influence drug discovery?

The quest for magic bullets started with Paul Ehrlich. He wanted to target the syphilis bacteria without harming the patients, which led to Salvarsan. Ehrlich was a foundational thinker, and we owe him modern concepts such as receptor and pharmacophore. His impact on the field – seeking the ideal molecules – pervades drug discovery.

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Looking to the future of Medicinal Chemistry

What is the future of medicinal chemistry? Ahead of the executive panel discussion at the upcoming Global Medicinal Chemistry & GPCR Summit, we spoke to David Powell of Inception Sciences Canada about his work and his predictions for the future.

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