This one struck a nerve, but there was some misinterpretation -- I'm not suggesting dry lab teams blame their wet lab counterparts! Rather, science is noisy and difficult, so let's not add additional sources of variation at the analysis steps. Standardize what you can and your AI/ML initiatives will be much better off. This requires better tooling, yes, but it also requires education and teams to work together. If your tools don't bring together your teams, then you probably have the wrong tools. The most successful companies of the next decade will be ones which blend software and science natively.
Unpopular opinion: Most biotech companies aren't ready for AI/ML, and it's not because of their data scientists. It's because of their bench scientists. Here's why: AI/ML requires large, consistent datasets. But in most biotechs, every scientist analyzes their assays slightly differently. They use different thresholds, different ways of normalizing data, different ways of identifying outliers — even different formulas for basic calculations. This inconsistency makes it impossible to compare results across experiments, let alone build AI models. "But we have SOPs!" you might say. Sure, for running the experiments. But for analyzing the data? Rarely. The hard truth is that without standardized analysis protocols, your data is useless for AI/ML. And implementing those protocols means taking away some of the autonomy bench scientists are used to. It's a tough pill to swallow, but it's necessary if biotechs want to harness the power of AI. At Sphinx Bio, we're building tools that make it easy to implement and enforce these protocols, without sacrificing the flexibility scientists need for exploratory analysis.
AI x Bio | Macro Data Refiner
4moI think you’re walking on thin ice… 🙃