Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Feb 24, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

ProtoBERT-LoRA: Parameter-Efficient Prototypical Finetuning for Immunotherapy Study Identification.

Shijia Zhang¹, Xiyu Ding¹, Kai Ding²

¹Johns Hopkins University School of Medicine, Baltimore, MD.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 23, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Improving Models to Predict Care Utilization Using Machine Learning: Retrospective Observational Study.

JMIR formative research·2026

Same author

Charting the future of ACMI: a report from the 2025 ACMI symposium.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

Identifying Clinical Diagnostic Trajectories Associated With Suicide Death Using Temporal Sequence Mining of Linked Claims and Mortality Data.

medRxiv : the preprint server for health sciences·2026

Same author

Improving models to predict care utilization using machine learning: a retrospective observational study.

JMIR formative research·2026

Same author

Conceptualization and development of the Maryland suicide data warehouse to improve mortality prediction.

JAMIA open·2026

Same author

Evaluating the role of cost and fairness in machine learning models of suicide risk.

Scientific reports·2026

Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

See all related articles

This summary is machine-generated.

Identifying immune checkpoint inhibitor (ICI) studies is crucial for cancer research. ProtoBERT-LoRA, a novel framework, efficiently identifies these studies, significantly reducing manual review efforts.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Identifying immune checkpoint inhibitor (ICI) studies in genomic repositories is vital for cancer research.
Challenges include semantic ambiguity, class imbalance, and limited labeled data.

Purpose of the Study:

To develop an efficient framework for identifying ICI studies in genomic repositories.
To overcome limitations of existing methods in low-resource settings.

Main Methods:

A hybrid framework, ProtoBERT-LoRA, combining PubMedBERT with prototypical networks and Low-Rank Adaptation (LoRA).
Episodic prototype training to enforce class-separable embeddings while preserving domain knowledge.
Utilized a dataset with specific positive/negative sample distributions for training, prototyping, validation, and testing.

More Related Videos

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Author Spotlight: Optimized Protocol for Detecting Antigen-Specific T Cells in Mouse Lungs Using Tetramers

Author Spotlight: Optimized Protocol for Detecting Antigen-Specific T Cells in Mouse Lungs Using Tetramers

Published on: July 19, 2024

Related Experiment Videos

Last Updated: Feb 24, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Author Spotlight: Optimized Protocol for Detecting Antigen-Specific T Cells in Mouse Lungs Using Tetramers

Author Spotlight: Optimized Protocol for Detecting Antigen-Specific T Cells in Mouse Lungs Using Tetramers

Published on: July 19, 2024

Main Results:

ProtoBERT-LoRA achieved an F1-score of 0.624 (precision: 0.481, recall: 0.887) on the test dataset.
Outperformed rule-based systems, machine learning baselines, and finetuned PubMedBERT.
Reduced manual review efforts by 82% when applied to unlabeled studies.
Combining prototypes with LoRA improved performance by 29% over stand-alone LoRA.

Conclusions:

ProtoBERT-LoRA offers an effective and efficient solution for identifying ICI studies in large genomic datasets.
The hybrid approach significantly enhances performance compared to existing methods.
This framework has the potential to accelerate cancer research by streamlining data analysis.