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Found 7 papers, 2 papers with code
Beyond the training set: an intuitive method for detecting distribution shift in model-based optimization
A common scenario involves using a fixed training set to train models, with the goal of designing new samples that outperform those present in the training data.
Contrastive losses as generalized models of global epistasis
Here we demonstrate that minimizing contrastive loss functions, such as the Bradley-Terry loss, is a simple and flexible technique for extracting the sparse latent function implied by global epistasis.
Forecasting labels under distribution-shift for machine-guided sequence design
The ability to design and optimize biological sequences with specific functionalities would unlock enormous value in technology and healthcare.
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design
In this work, we implement an open-source Fitness Landscape EXploration Sandbox (FLEXS: github. com/samsinai/FLEXS) environment to test and evaluate these algorithms based on their optimality, consistency, and robustness.
A primer on model-guided exploration of fitness landscapes for biological sequence design
This primer can serve as a starting point for researchers from different domains that are interested in the problem of searching a sequence space with a model, but are perhaps unaware of approaches that originate outside their field.
Variational auto-encoding of protein sequences
Here we present an embedding of natural protein sequences using a Variational Auto-Encoder and use it to predict how mutations affect protein function.
Primordial Sex Facilitates the Emergence of Evolution
Compartments are ubiquitous throughout biology, yet their importance stretches back to the origin of cells.
