Earticle

초록

영어

In this study, we propose a semi-supervised dataset curation framework that leverages both high-confidence labeled protein sequence data and automated weakly labeled protein sequence data to refine dataset quality prior to model training. The approach centers on using a pre-trained ProtBERT model to iteratively assign pseudo-labels to uncertain samples, followed by subsequent model retraining, with the goal of enhancing robustness and generalization. We anticipate that a curated dataset constructed in this way will significantly enhance toxin-classification performance— measured in accuracy, F1-score, and MCC—compared to models trained solely on manually labeled or automatically annotated data.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
A. Semi-supervised learning
B. Protein Sequence Models
III. METHODOLOGY
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Sung-Yoon Ahn [ School of Computing, Gachon University Seongnam-Si, Republic of Korea ]
• Sewon Kim [ School of Computing,, Gachon University Seongnam-Si, Republic of Korea ]
• Hye Won Jeong [ Department of Microbiology and Immunology Chosun University School of Dentistry Gwangju. Korea ]
• Sang-Woong Lee [ School of Computing,, Gachon University Seongnam-Si, Republic of Korea ]
• Iel Soo Bang [ Department of Microbiology and Immunology Chosun University School of Dentistry Gwangju. Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004