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Three best practices for applying machine learning in the life sciences

Three best practices for applying machine learning in the life sciences

BioLizard shares best practices for applying AI in life sciences: avoid reinventing the wheel, integrate deep biological knowledge, and ensure high-quality, well-managed data. By following these steps, companies can harness AI effectively, avoid pitfalls, and generate actionable, data-driven insights.

Machine learning for protein engineering and design: Beyond AlphaFold

Machine learning for protein engineering and design: Beyond AlphaFold

BioLizard shows how machine learning can go beyond AlphaFold for protein engineering. ML streamlines pipelines, predicts beneficial protein properties, optimises synthetic libraries, and accelerates personalised medicine, helping life sciences companies save time, reduce costs, and boost experimental success.

Transforming data into value with innovative tech

Transforming data into value with innovative tech

Volodimir Olexiouk leads BioLizard’s Data Analytics & AI team, combining deep biology, bioinformatics, and machine learning expertise. The team collaborates across disciplines to transform complex biological data into actionable insights, accelerating R&D and creating measurable value for clients.

How we tackle R&D data management challenges in R&D

How we tackle R&D data management challenges in R&D

Yves Muyssen, BioLizard COO and Data Management lead, leverages his expertise to help life sciences clients tackle complex R&D data challenges. By assembling cross-functional teams and implementing FAIR principles, BioLizard ensures data is structured, accessible, and ready for AI-driven insights.

Maintaining your R&D data management plan

Maintaining your R&D data management plan

BioLizard helps life sciences organizations maintain effective R&D data management plans, ensuring FAIR, compliant, and scalable data practices. By training internal data stewards and designing systems that handle routine tasks automatically, teams save time, reduce errors, and unlock AI-driven insights.

A step-by-step guide to R&D data management

A step-by-step guide to R&D data management

BioLizard guides life sciences organizations through step-by-step R&D data management, aligning IT and R&D, implementing FAIR principles, and creating actionable roadmaps. Their approach improves efficiency, scalability, and scientific insights while enabling long-term, data-driven innovation.