Speaker Profile
CEO, Magna Labs
Biography
Gwenn Berry is the founding engineer and CEO of Magna Labs Inc., whose mission is to accelerate the innovation and delivery of precision science medicine by empowering scientists to build and test high-quality bioinformatics tools. Gwenn has over a decade of experience in applying and adapting software best practices to bioinformatics algorithm development and transitioning research software into both commercial research-use-only (RUO) and in-vitro diagnostic (IVD) on-market products in the genomics industry. Her work specializes in production-grade bioinformatics algorithm and pipeline development, including methods evaluation and optimization, novel methods development, verification and validation, and remediation of prototype or research-grade software for commercial and clinical readiness.
Talk
Automating bioinformatic software validation for production-grade development
Bioinformatics software drives omics research and development, yet comprehensive software QA is often ad hoc, manual and time-intensive. Miqa, a no-code, cloud-enabled QA automation platform, continuously tests and benchmarks bioinformatic algorithms, ensuring development speed, analytical accuracy, and data reproducibility to transform genomic information into actionable insights for precision medicine.
AI and Data Sciences Showcase:
Magna Labs
Magna Labs provides software quality assurance (QA) solutions to support the safe development and evaluation of bioinformatic software for precision therapeutics and diagnostics. Our software tools help scientists easily incorporate software testing best practices into their workflows, improving the quality and speed of scientific innovations.
Session Abstract – PMWC 2025 Silicon Valley
The PMWC 2024 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
