Biologists

Computational biologists create reusable workflows that embed and scale their expertise in model selection, pipeline design, and analytical rigor, encoding decisions like which AI biology models to chain together, how to process and validate ...

“This was around the time when the idea of integrating AI with biology was starting to pick up,” Lu recalls. “Tristan helped us build better computational models for biologic design. We also realized there’s a disconnect between the most cutting-edge tools available and the biologists, who would love to use these things but don’t know how to code.

OpenProtein.AI is helping biologists stay on the cutting edge of AI with a no-code platform for protein engineering. It was founded by MIT alumni Tristan Bepler and Tim Lu.

As science delivers a deeper understanding of how biological language is translated into physical structure, the toolkits of future synthetic biologists are being built. The field of synthetic biology is approaching a tipping point driven by the application of ML31. Revolutionary ability to augment and automate computational steps in the design-build-test-learn pipeline will be delivered by AI32.

This, in turn, is critical for developing databases that are massive and comprehensive enough to drive useful ML models and powerful AI algorithms. The Open Datasets Initiative of The Align Foundation (Cambridge, MA) operates with a mandate to do just this: it brings together biologists, machine learning specialists, and automation experts to develop protocols for use in automated labs to collect high-fidelity, AI-ready biological datasets.

Then, once you train a model, you need to know it actually works as intended. So there’s a team of biologists – computational, molecular, systems, prokaryotic, eukaryotic biologists – to make sure the information we are getting back is valuable and usable.

The goals of applying of AI in biology go beyond prediction or classification. Biologists are eager to use AI for learning biological knowledge from their data and guiding them to design new experiments and translational strategies. The black-box nature of many machine learning approaches therefore ...

So, it started as a tool that we ... serve much more.” · BioAutoMATED is an integrated AutoML tool that allows biologists to analyze biological sequences and extract insights from large datasets that can be used to inform ...

Assemblers, inspectors, and attendants were predicted to be almost certainly replaced by automation in the next 10 to 20 years. Biologists and related scientists were predicted to have a 15.6% chance of being replaced by automation.