arXiv (All)
open access
Improving Sample Efficiency in Peptide-HLA Binding Prediction with Hybrid Quantum-Classical Neural Networks
Peptide-HLA binding prediction is a critical step in neoantigen identification for personalized cancer immunotherapy and holds significant clinical value. However, the training data available for many HLA alleles are extremely limited, which severely constrains the performance of conventional method
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- NetMHCIIphosPan: a machine learning tool for predicting HLA class II antigen presentation of phosphorylated peptides#300705
- Paraplume: A fast and accurate antibody paratope prediction method provides insights into repertoire-scale binding dynamics#156264
- DuaDeep-SeqAffinity: Dual-Branch Deep Learning for Tri-Stream Sequence-Based Antibody--Antigen Affinity Prediction#925882
- A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation.#436986
- A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction#271888
Record · ID 1014231
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive