Research graph
References from FLIGHTED: Inferring fitness landscapes from noisy high-throughput experimental data. Local targets link to admitted publications; unresolved targets remain external evidence.
Machine learning for protein engineering
2023 · External reference
Deep diversification of an AAV capsid protein by machine learning
10.1038/s41587-020-00793-4 · 2021 · External reference
Low-N protein engineering with data-efficient deep learning
10.1038/s41592-021-01100-y · 2021 · External reference
Machine learning-assisted directed protein evolution with combinatorial libraries
10.1073/pnas.1901979116 · 2019 · External reference
Feature reuse and scaling: understanding transfer learning with protein language models
2024 · External reference
FLIP: benchmark tasks in fitness landscape inference for proteins
2021 · External reference
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
10.1073/pnas.2016239118 · 2021 · External reference
MSA Transformer
2021 · External reference
ProGen2: exploring the boundaries of protein language models
10.1016/j.cels.2023.10.002 · 2023 · External reference
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
2022 · External reference
Unsupervised inference of protein fitness landscape from deep mutational scan
10.1093/molbev/msaa204 · 2021 · External reference
A statistical framework for analyzing deep mutational scanning data
10.1186/s13059-017-1272-5 · 2017 · External reference
Model-based differential sequencing analysis
10.1186/s13059-023-03058-w · 2023 · External reference
Inference of annealed protein fitness landscapes with AnnealDCA
10.1371/journal.pcbi.1011812 · 2024 · External reference
Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy
2024 · External reference
Bayesian inference of relative fitness on high-throughput pooled competition assays
10.1371/journal.pcbi.1011937 · 2024 · External reference
An ultra-high-throughput method for measuring biomolecular activities
2024 · External reference
Stochastic variational inference
2013 · External reference
Auto-encoding variational Bayes
2014 · External reference
Learning structured output representation using deep conditional generative models
2015 · External reference
A mathematical model for biopanning (affinity selection) using peptide libraries on filamentous phage
10.1006/jtbi.1995.0218 · 1995 · External reference
Introduction to phage biology and phage display
2004 · External reference
A statistical guide to the design of deep mutational scanning experiments
10.1534/genetics.116.190462 · 2016 · External reference
Adaptation in protein fitness landscapes is facilitated by indirect paths
10.7554/elife.16965 · 2016 · External reference
Phage-assisted continuous and non-continuous evolution
10.1038/s41596-020-00410-3 · 2020 · External reference
Nature, nurture, or chance: stochastic gene expression and its consequences
10.1016/j.cell.2008.09.050 · 2008 · External reference
Language models enable zero-shot prediction of the effects of mutations on protein function
2021 · External reference
Convolutions are competitive with transformers for protein sequence pretraining
10.1016/j.cels.2024.01.008 · 2024 · External reference
Learning protein fitness models from evolutionary and assay-labeled data
10.1038/s41587-021-01146-5 · 2022 · External reference
ProteinGym: large-scale benchmarks for protein fitness prediction and design
2023 · External reference
Monte Carlo error analyses of Spearman’s rank test
2015 · External reference
Language models of protein sequences at the scale of evolution enable accurate structure prediction
10.1126/science.ade2574 · 2022 · External reference
Pyro: deep universal probabilistic programming
2019 · External reference
PyTorch: an imperative style, high-performance deep learning library
2019 · External reference
SciPy 1.0: fundamental algorithms for scientific computing in Python
10.1038/s41592-019-0686-2 · 2020 · External reference
Evaluating protein transfer learning with TAPE
2019 · External reference
ProtTrans: toward understanding the language of life through self-supervised learning
10.1109/tpami.2021.3095381 · 2022 · External reference
Mutation effects predicted from sequence co-variation
10.1038/nbt.3769 · 2017 · External reference
