Episodes

Technologies for therapeutic antibody R&D have evolved rapidly, but the informatics needed to support these processes have struggled to keep up. In this episode, we discuss challenges in antibody discovery and benefits of improved informatics.

Data liquidity, the ability of data to flow easily and securely, provides tremendous value for biopharma researchers. Open access is paving the way for increased collaboration and discovery. However, additional initiatives are needed to further promote the management and sharing of scientific data and ensure that information is quickly available to researchers. This episode highlights the needs, solutions, and challenges of ensuring interoperability of research data throughout the data lifecycle...

Data liquidity, the ability of data to flow easily and securely, provides tremendous value for biopharma researchers. Open access is paving the way for increased collaboration and discovery. However, additional initiatives are needed to further promote the management and sharing of scientific data and ensure that information is quickly available to researchers. This episode highlights the needs, solutions, and challenges of ensuring interoperability of research data throughout the data lifecycle...

Data liquidity, the ability of data to flow easily and securely, provides tremendous value for biopharma researchers. Open access is paving the way for increased collaboration and discovery. However, additional initiatives are needed to further promote the management and sharing of scientific data and ensure that information is quickly available to researchers. This episode highlights the needs, solutions, and challenges of ensuring interoperability of research data throughout the data lifecycle...

Increasingly, pharma is looking at partnerships and collaborations to increase the velocity of drug discovery and development. Common data sharing platforms are not built to meet lab-specific requirements. In addition to discussing the necessity of simple and secure collaboration, listeners will learn about the dangers of not using proper tools.

Increasingly, pharma is looking at partnerships and collaborations to increase the velocity of drug discovery and development. Common data sharing platforms are not built to meet lab-specific requirements. In addition to discussing the necessity of simple and secure collaboration, listeners will learn about the dangers of not using proper tools.

Increasingly, pharma is looking at partnerships and collaborations to increase the velocity of drug discovery and development. Common data sharing platforms are not built to meet lab-specific requirements. In addition to discussing the necessity of simple and secure collaboration, listeners will learn about the dangers of not using proper tools.

As biopharma research and development continues to transform digitally, the industry as a whole struggles with guidance on how data and relevant metadata are captured, managed, and shared. Typically experimental data are siloed,

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 discove...

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?

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.