What Should I Do Next? How Democratized Machine Learning Supports Decision Making in Biopharmaceuticals R&D
Machine learning and Big Data analytics offer significant opportunities to improve biopharmaceuticals R&D, providing scientists with a new set of tools to analyze their data. These approaches can help scientists do more with less, building a stronger, data-driven foundation for decision making and guiding future research. However, traditional methods to apply these techniques required extensive custom coding, deep technical knowledge, and prohibitively large data sets to create an effective model.
Recent advances in machine learning and Big Data analytics have helped to mitigate these requirements, with tools designed specifically for biopharmaceuticals-focused data science. With these, scientists can develop custom models and algorithms much faster than before – no matter the size of their data sets – and can easily share them with their colleagues to ensure best practices are conserved across a research group.
This webinar will explore some use cases to show how machine learning and Big Data analytics can help overcome the challenges facing the biopharmaceutical industry through more confident data-driven decisions. It will also cover how data science pipelining tools can help make these techniques more accessible – both by simplifying the model design process and outlining approaches for differently sized data sets.
Presented by
Sean McGee,
Product Marketing Manager РLife Sciences R&D, Dassault Syst̬mes BIOVIA
Sean McGee is the Product Marketing Manager for BIOVIA’s Life Science R&D portfolio, specializing in machine learning and data analytics, biologic and materials modeling and simulation, data visualization, and cloud-enabled collaborative workspaces.
His primary interest is in fostering the commercialization of technologies that can fundamentally impact how biopharmaceutical R&D is carried out. Prior to his time at BIOVIA, Sean received a Master of Science degree from the University of Notre Dame focusing on the applications of stochastic Monte-Carlo methods to model the propagation of photons throughout human tissue.
