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Linear-scaling kernels for protein sequences and small molecules
  outperform deep learning while providing uncertainty quantitation and
  improved interpretability

Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability

7 February 2023
J. Parkinson
Wen Wang
    BDL
ArXivPDFHTML

Papers citing "Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability"

2 / 2 papers shown
Title
Kermut: Composite kernel regression for protein variant effects
Kermut: Composite kernel regression for protein variant effects
Peter Mørch Groth
Mads Herbert Kerrn
Lars Olsen
Jesper Salomon
Wouter Boomsma
39
2
0
09 Apr 2024
Tranception: protein fitness prediction with autoregressive transformers
  and inference-time retrieval
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Pascal Notin
M. Dias
J. Frazer
Javier Marchena-Hurtado
Aidan N. Gomez
D. Marks
Y. Gal
55
177
0
27 May 2022
1