antibodies

Boosting Machine Learning in Biotech with Prediction Stability

📚 Researchers have developed an approach to improve machine learning in biological sequence design. 👥 By considering prediction stability, the selection pool of antibodies can be expanded. 💡 This method allows machine learning algorithms to identify diverse sequences unrelated to training data. ⚖️ The approach balances safety and success by finding a middle ground between prediction accuracy and uncertainty. 🧪 Scientists validated this approach by designing antibodies against galectin-3 and successfully expressing a desired sequence. 🔬 The researchers used their own multi-objective optimization software, but any off-the-shelf optimizer can be used. 🌐 This approach can be particularly useful in situations with limited data.