Earticle

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

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

참고문헌