BioLizard – Making the most of your scientific data

BioLizard helps scientists maximize the value of their biomedical data. Offering tailored solutions in data management, architecture, visualization, analytics, and interpretation, BioLizard supports data-driven success in the increasingly complex and dynamic life sciences landscape.

The industry is becoming extremely data-driven, and the integration of heterogeneous data in a dynamic environment is becoming central to success.

At BioLizard, we provide innovative solutions to diverse biological questions, including data management, data architecture, data visualisation, data analytics, and data interpretation. All of our services are tailored to your needs, to help you make the most of your biomedical data.

How spatial biology improves clinical trial success in oncology

How spatial biology improves clinical trial success in oncology

In oncology, the drug development path is unique: Phase 0 and Phase I trials are typically conducted in patients rather than healthy volunteers, allowing for early assessment of efficacy and patient selection alongside safety. Yet, even with this early clinical insight, many cancer drugs show promise in the lab but fail to transition effectively into the clinic. This often happens because, while we verify that a drug’s target is present, we frequently overlook its context, specifically its location, the surrounding microenvironment, and its interaction with neighboring cells. By revisiting real-world examples of discontinued trials, this post explains why understanding the “where” is just as critical as the “what”, and how spatial biology is positioning itself as a valuable avenue for validating clinical potential.

Why bioinformatics workflows require experienced software engineers

Bioinformatics pipelines break for the smallest reasons: package updates, shifting dependencies, or “it only works on my machine.” This post explains why experienced software engineers and DevOps practices (Git, CI/CD, IaC) are essential to keep workflows reproducible, stable, and scalable.