Julia Fukuyama

Immunology and Microbiology · Indiana University

T-cell and B-cell Immunology Monoclonal and Polyclonal Antibodies Research Gut microbiota and health Gene expression and cancer classification Genomics and Phylogenetic Studies

Publications

Citations

3,069

Est. group size

—

Recurring co-author estimate

Active years

Publishing since 2007

Research summary

AI-generated

Julia Fukuyama develops computational and statistical methods to study how the immune system works, with a particular focus on antibodies and B cells. Much of her recent work uses machine learning models to understand how antibodies mutate and improve over time (a process called affinity maturation), and she also builds tools for analyzing biological data such as gut microbiome communities and genomic sequences.

Computational immunology and antibody modelingMachine learning for biological sequencesB cell receptor mutation and affinity maturationStatistical methods for microbiome and genomic dataModel interpretation and evaluation methods

After several years of modest output (about 2 papers per year from 2021 to 2024), publication activity increased sharply in 2025 and 2026.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence

Publications per year over the last 10 years — averaging 6.0/year recently

Recent publications

Separating selection from mutation in antibody language models
eLife · 2026
Separating selection from mutation in antibody language models
eLife · 2026
Separating selection from mutation in antibody language models
eLife · 2026
Author response: Separating selection from mutation in antibody language models
2026
A comparative analysis of machine learning models in SHAP analysis
arXiv (Cornell University) · 2026
A comparative analysis of machine learning models in SHAP analysis
arXiv (Cornell University) · 2026
Entrenchment of germline amino-acid differences in antibody affinity maturation
bioRxiv (Cold Spring Harbor Laboratory) · 2026
A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer–Encoder
Molecular Biology and Evolution · 2025
Thrifty wide-context models of B cell receptor somatic hypermutation
eLife · 2025
Inferring mechanistic parameters of somatic hypermutation using neural networks and approximate Bayesian computation
The Annals of Applied Statistics · 2025
Thrifty wide-context models of B cell receptor somatic hypermutation
eLife · 2025
Nucleotide context models outperform protein language models for predicting antibody affinity maturation
PLoS Computational Biology · 2025
Nucleotide context models outperform protein language models for predicting antibody affinity maturation
bioRxiv (Cold Spring Harbor Laboratory) · 2025
A decomposition of a phylogenetically-informed distance into basal and terminal components
bioRxiv (Cold Spring Harbor Laboratory) · 2025
Author Response: Thrifty wide-context models of B cell receptor somatic hypermutation
2025

Publishes in

arXiv (Cornell University)×9
eLife×7
bioRxiv (Cold Spring Harbor Laboratory)×5
PLoS Computational Biology×4
Zenodo (CERN European Organization for Nuclear Research)×4

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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