원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 8th International Conference on Next Generation Computing 2022
2022.10
pp.319-320
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보