The 8th International Conference on Next Generation Computing 2022 (2022.10)바로가기
페이지
pp.319-320
저자
Sung-Yoon Ahn, Sung-Hoon Kim, Ji-Soo Tak, Sang-Woong Lee
언어
영어(ENG)
URL
https://www.earticle.net/Article/A419812
원문정보
초록
영어
From Covid-19 we have witnessed the destructive power of infectious diseases. To prevent such catastrophes from occurring, it is crucial to prevent an outbreak of any infectious disease. As it is well known for bacteria and viruses to cause such outbreaks, some fungal species also cause harmful reactions. In this paper, we attempt to classify toxic fungi protein sequences through the help of protBERT a BERT-based protein language model. Our experiment results reveal the effectiveness of our proposed approach as it shows 99% accuracy and F1 score of 0.9901 in the classification of toxic fungi protein sequences.
목차
Abstract I. INTRODUCTION II. RELATED WORKS III. MATERIALS AND METHOD A. Dataset and Dataset collection B. Model description C. Evaluation metrics D. Experiment harware and hyper parameter setup IV. RESULTS V. CONCLUSION REFERENCES
키워드
Fungiproteintoxin
저자
Sung-Yoon Ahn [ Pattern Recognition and Machine learning Lab Gachon University Gyeonggi-do, Republic of Korea ]
Sung-Hoon Kim [ Pattern Recognition and Machine learning Lab Gachon University Gyeonggi-do, Republic of Korea ]
Ji-Soo Tak [ Pattern Recognition and Machine learning Lab Gachon University Gyeonggi-do, Republic of Korea ]
Sang-Woong Lee [ Pattern Recognition and Machine learning Lab Gachon University Gyeonggi-do, Republic of Korea ]
Corresponding Author