Investigating the determinants of performance in machine learning for protein fitness prediction

Code for evaluating ML architectures on NK fitness landscapes, benchmarking interpolation, extrapolation, ablation, and positional extrapolation performance.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-18
DOI
https://doi.org/10.5281/zenodo.15589853
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Investigating the determinants of performance in machine learning for protein fitness prediction

Zenodo (CERN European Organization for Nuclear Research)
Evolutionary Algorithms and Applications
article

Investigating the determinants of performance in machine learning for protein fitness prediction

article en

Abstract

Code for evaluating ML architectures on NK fitness landscapes, benchmarking interpolation, extrapolation, ablation, and positional extrapolation performance.

Zenodo (CERN European Organization for Nuclear Research)
Australian National University (AU), UNSW Sydney (AU), ARC Centre of Excellence in Synthetic Biology (AU), ARC Centre of Excellence for Innovations in Peptide and Protein Science (AU)
Openalex Percentile: Top 76%
Evolutionary Algorithms and Applications
DOIOpen Access PDFOpenAlex
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Investigating the determinants of performance in machine learning for protein fitness prediction · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS