Data talks with BioLizard: The power of proteomics

In this episode of Data Talks with BioLizard, experts Lennart Martens and Gerben Menschaert explore proteomics, highlighting how data science analyzes vast protein datasets, discusses emerging trends and technologies, and examines the challenges and opportunities shaping the future of the field.

In this episode, we dove deep into the world of proteomics, discussing how data science is being used to analyze the vast amounts of protein data generated by this cutting-edge field.

We are joined by leading experts in the field of data science and proteomics, Lennart Martens, professor at Ghent University and head of the Computational Omics and Systems Biology group, and Gerben Menschaert, Associate Professor at Ghent University and CSO of OHMX.bio, have both have made significant contributions to the proteomics field through their research, who share their insights on the latest trends and technologies, as well as the challenges and opportunities that lie ahead.

https://open.spotify.com/embed/episode/10UQQwiLQGe4fpSBwyV4wQ?si=x8R0OW1xSvua422aJD63FQ&utm_source=oembed

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.