FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data
Vikram Sundar,
Boqiang Tu,
Lindsey Guan
et al.
Abstract:Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training, fine-tuning, and bench-marking models. However, most models do not account for experimental noise inherent in these datasets, harming model performance and changing model rankings in benchmarking studies. Here, we develop FLIGHTED, a Bayesian method for generating fitness landscapes with calibrated errors from noisy high-throughput experimental data. We apply FLIGHTED to singl… Show more
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