pharmaphorum
JANUARY 11, 2023
Over the past few decades, data generation has veritably exploded. However, the ‘Big Data paradigm’ is not so much concerned with the volume of that data, but how businesses and, indeed, industries can derive meaningful insights from what has become a glut of information. From Big Data to small.
Drug Discovery World
JULY 11, 2023
Tim Lowery , President, JSR Life Sciences, asks whether artificial intelligence can do for life sciences what it has done in other sectors and whether these tools can keep up with the complexities of human biology. So, where do we go from here to realise their potential in life sciences?
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Drug Discovery World
FEBRUARY 6, 2023
Johannes Goll, Global Head of Emmes’ Biomedical Data Science and Bioinformatics department, explains how big data analytics can deliver improved therapies and bring clinical research one step close to realising the potential of personalised care. He holds an MS degree in Statistics and a BS-equivalent in Bioengineering.
pharmaphorum
FEBRUARY 1, 2022
David Clifton is professor of clinical machine learning in the Department of Engineering Science of the University of Oxford. His research focuses on the development of big data machine learning for tracking the health of complex systems. Mark holds a PhD in biomedicine, and an MSc in biostatistics.
The Pharma Data
JANUARY 17, 2021
There has never been a time when rapid, low burden access to patient-level data, at scale, was more urgent. rows of data. Syntegra has also engaged with the Federal Drug Administration (FDA) to evaluate the role of synthetic data in regulatory decisions, for COVID-19 and beyond. .
XTalks
APRIL 5, 2021
Bioinformaticians use a combination of mathematics, computer science and biology to help scientists make sense of the data gathered from research projects. The findings gathered are then documented and presented to a larger audience, whether to scientists in the lab or at conferences. Bioinformatics Scientist.
pharmaphorum
FEBRUARY 11, 2022
Therefore, organisations can determine how they’d like to use the Map’s data then create a software solution that helps them do so. This technology can be particularly beneficial for rare disease organisations as they often don’t have data scientists at their disposal or full-stack engineers. About the author.
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