Paper Citation Record · LEDGER

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation

As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.16921.

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pith.paper-citation-record.v1
2506.16921 v1

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Outbound references

Observation 3e5a0539-3db9-49c1-bc35-9de88ee7a399 · outbound

This paper cites Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

Reference 1

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Observation 8ea8d7ea-d3d8-4a46-91bc-86af2a23af8b · outbound

This paper cites Machine learning to navigate fitness landscapes for protein engineer- ing.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Machine learning to navigate fitness landscapes for protein engineer- ing

Reference 2

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This paper cites Protein engineering in the deep learning era.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Protein engineering in the deep learning era

Reference 3

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This paper cites In: Currin A, Swainston N, editors.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation In: Currin A, Swainston N, editors

Reference 4

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Observation 5a5e2f9b-d114-4581-8707-3a6ffbaee389 · outbound

This paper cites From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

Reference 5

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Observation ff044c67-c90f-408e-b8dc-0e563301732b · outbound

This paper cites Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering

Reference 6

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Observation 933a5062-e922-4f55-b15b-fe3521901f7e · outbound

This paper cites Exploring protein fitness landscapes by directed evolution.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Exploring protein fitness landscapes by directed evolution

Reference 7

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This paper cites A review on multiple sequence alignment from the perspective of genetic algorithm.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A review on multiple sequence alignment from the perspective of genetic algorithm

Reference 8

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This paper cites VISUALIZING FITNESS LANDSCAPES.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation VISUALIZING FITNESS LANDSCAPES

Reference 9

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Observation 2e7e73f6-847c-41a3-8d5c-0e09a79ca943 · outbound

This paper cites Learning the pattern of epistasis linking genotype and phenotype in a protein.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Learning the pattern of epistasis linking genotype and phenotype in a protein

Reference 10

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This paper cites A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes

Reference 11

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correction dated 2021-04-12. Source: crossref record 10.1038/s41598-021-86884-x->10.1038/s41598-018-35033-y:correction, observed 2026-07-11T03:09:49.715478+00:00. This notice travels one citation hop only.

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This paper cites Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness

Reference 12

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This paper cites Low-N protein engineering with data-efficient deep learning.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Low-N protein engineering with data-efficient deep learning

Reference 13

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Observation b013c521-fda5-4df9-ac73-0346488787c4 · outbound

This paper cites Learning protein fitness models from evolutionary and assay- labeled data.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Learning protein fitness models from evolutionary and assay- labeled data

Reference 14

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Observation 4c73ac5d-633c-48a9-b3bd-2cae45357c44 · outbound

This paper cites Higher-orderepistasisandphenotypic prediction.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Higher-orderepistasisandphenotypic prediction

Reference 15

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This paper cites A Gentle Introduction to Graph Neural Networks.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A Gentle Introduction to Graph Neural Networks

Reference 16

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This paper cites Graph Neural Networks and Their Current Applications in Bioinformatics.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Graph Neural Networks and Their Current Applications in Bioinformatics

Reference 17

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This paper cites ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks

Reference 18

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This paper cites Quantitative exploration of the catalytic landscape separating divergent plant sesquiterpene synthases.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Quantitative exploration of the catalytic landscape separating divergent plant sesquiterpene synthases

Reference 19

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This paper cites Biosynthetic potential of sesquiterpene synthases: product profiles of Egyptian Henbane premnaspirodiene synthase and related mutants.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Biosynthetic potential of sesquiterpene synthases: product profiles of Egyptian Henbane premnaspirodiene synthase and related mutants

Reference 20

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This paper cites In: Sikosek T, editor.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation In: Sikosek T, editor

Reference 21

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This paper cites Wavelets: Mathematical Theory.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Wavelets: Mathematical Theory

Reference 22

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This paper cites A wavelet tour of signal processing.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A wavelet tour of signal processing

Reference 23

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This paper cites A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities

Reference 24

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EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation 4.2 - Multiscale Image Decompositions and Wavelets

Reference 25

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This paper cites A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising

Reference 26

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This paper cites Ideal spatial adaptation by wavelet shrinkage.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Ideal spatial adaptation by wavelet shrinkage

Reference 27

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This paper cites Mechanisms of DNA damage, repair, and mutagenesis.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Mechanisms of DNA damage, repair, and mutagenesis

Reference 28

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EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Mutation, repair and recombination

Reference 29

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This paper cites The Context-Dependence of Mutations: A Linkage of Formalisms.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation The Context-Dependence of Mutations: A Linkage of Formalisms

Reference 30

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EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A new model for learning in graph domains

Reference 31

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Semi-Supervised Classification with Graph Convolutional Networks

Reference 32

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This paper cites A Flexible Generative Framework for Graph-based Semi-supervised Learning.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation A Flexible Generative Framework for Graph-based Semi-supervised Learning

Reference 33

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This paper cites The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains

Reference 34

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This paper cites Minimum epistasis interpolation for sequence-function relationships.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation Minimum epistasis interpolation for sequence-function relationships

Reference 35

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This paper cites https://academic.oup.com/evolut/article- pdf/65/6/1544/47949872/evolut1544.pdf.

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation https://academic.oup.com/evolut/article- pdf/65/6/1544/47949872/evolut1544.pdf

Reference 1558

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