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.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-18
DOI
https://doi.org/10.5281/zenodo.15589854
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
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 77%
Evolutionary Algorithms and Applications
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Investigating the determinants of performance in machine learning for protein fitness prediction · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS