Earticle

초록

영어

Although living organisms differ in shape and size, all are fundamentally structured by genetic sequences. Interpreting these sequences helps explain how organisms function. With the advancement of AI, significant breakthroughs have been made in protein sequencing and understanding protein function. However, there is still room for improvement, as data-intensive models require a substantial amount of protein sequences, many of which are not publicly available or lack quality. In this paper, we present a semi-supervised learning scheme to address the shortage of high-quality training data necessary for training protein language models. We demonstrate that this approach enhances the model's capability to classify toxic fungi protein sequences.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
III. MATERIALS AND METHOD
A. Dataset and Dataset Collection
B. Semi-supervised learning scheme
C. Model selection
IV. RESULTS
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Sung-Yoon Ahn [ School of Computing, Gachon University Seongnam-Si, Republic of Korea ]
• Chan-Young Choi [ School of Computing Gachon University Seongnam-Si, Republic of Korea ]
• Abrar Alabdulwahab [ School of Computing Gachon University Seongnam-Si, Republic of Korea ]
• Joo-Hee Oh [ School of Computing Gachon University Seongnam-Si, Republic of Korea ]
• Sang-Woong Lee [ School of Computing,, Gachon University Seongnam-Si, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004