Sharing and collaboration are increasing internally across biopharma research & development (R&D) groups. How is pharma currently dealing with these challenges? What are the various methods and tools, along with their advantages?

Capture, management, and sharing of research data and metadata are a growing concern for the biopharma industry. Learn how data standardization can facilitate collaboration and improve accessibility.

Sequencing is used to identify novel drug targets and genetically stratifying clinical trial patients, improving the power of precision medicine. However, the global disparity of genomic technologies is increasing. How will this affect drug development, particularly for non-European ethnicities? Why is it important to have a global context for targeted therapies?

Life science companies are looking to modernize their research environments and build the Lab of the Future. From early discovery to preclinical research, how can technology help automate the lab and accelerate discoveries?

What are the theories, algorithmic principles, and limitations of active learning? What are some current applications of active learning within BioPharma drug screening and optimization? How can active learning accelerate multi-dimensional drug discovery? Find out here.

What is the vision of interactive data exploration beyond enriching visualizations and facilitating the integration of data? Within the context of research, what are the challenges and opportunities in building next-generation visual analytics? Listen to find out.

Artificial Intelligence (AI) and Machine Learning (ML) are big topics getting lots of attention within BioPharma. What is the role of AI/ML in genomics? How can we separate the hype of AI/ML from reality? Listen in to find out this and more.

Why are research organizations moving to the Cloud? Learn why they decided to initiate the move, what issues they’ve tackled, what their biggest challenges were, and where they’re going next.