Exploring Parameter-Efficient Fine-Tuning of a Large-Scale Pre-Trained Model for scRNA-seq Cell Type Annotation-Reference-Cited by-同舟云学术

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Exploring Parameter-Efficient Fine-Tuning of a Large-Scale Pre-Trained Model for scRNA-seq Cell Type Annotation

Published:2023-12-05 Issue: Volume: Page:580-585
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Liu Yuhang¹,Li Tianhao¹,Wang Zixuan²,Zhu Guiquan³,Zhang Yongqing¹,Zou Quan⁴

Affiliation:

1. Chengdu University of Information Technology,School of Computer Science,Chengdu,China

2. Sichuan University,College of Electronics and Information Engineering,Chengdu,China

3. Sichuan University,West China Hospital of Stomatology,Chengdu,China

4. University of Electronic Science and Technology of China,Institute of Fundamental and Frontier Sciences,Chengdu,China

Funder

National Natural Science Foundation of China

Chengdu University of Information Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385599.pdf?arnumber=10385599

Reference24 articles.

1. Single-cell RNA sequencing in cardiovascular development, disease and medicine

2. Single‐cell RNA sequencing technologies and applications: A brief overview

3. Applications of single-cell RNA sequencing in drug discovery and development

4. Automated methods for cell type annotation on scRNA-seq data

5. Challenges in unsupervised clustering of single-cell RNA-seq data

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