Synthetic protein feature samples based on generative adversarial networks
Synthetic protein feature samples based on generative adversarial networks
## CTST2.py
## CTST2.py
Effector-GAN uses the leave-one-out cross-validation (LOOCV) method based on K nearest neighbor algorithm (KNN; K=1) classifier to evaluate the optimal synthetic protein feature samples, which are used to augment the original positive training samples
Effector-GAN uses the leave-one-out cross-validation (LOOCV) method based on K nearest neighbor algorithm (KNN; K=1) classifier to evaluate the optimal synthetic protein feature samples, which are used to augment the original positive training samples
## figure_tsne.py
t-SNE-transformed 2D visualization of real and synthetic protein feature samples obtained from different training iterations
## **If you use prPred, please cite:**
## **If you use prPred, please cite:**
(1) Wang Y, Wang P, Guo Y, et al. prPred: A Predictor to Identify Plant Resistance Proteins by Incorporating k-Spaced Amino Acid (Group) Pairs[J]. Frontiers in bioengineering and biotechnology, 2021, 8: 1593.
(1) Wang Y, .Effector-GAN: prediction of fungal effector proteins based on pre-trained deep representation learning methods and generative adversarial networks
(2) Yansu Wang, Murong Zhou, Quan Zou, Lei Xu. Machine learning for phytopathology: from the molecular scale towards the network scale. Briefings in Bioinformatics. 2021, Doi: 10.1093/bib/bbab037
(2) Yansu Wang, Murong Zhou, Quan Zou, Lei Xu. Machine learning for phytopathology: from the molecular scale towards the network scale. Briefings in Bioinformatics. 2021, Doi: 10.1093/bib/bbab037
(3) Yansu Wang, Lei Xu, Quan Zou, Chen Lin. prPred-DRLF: plant R protein predictor using deep representation learning features. Proteomics. 2021. DOI: 10.1002/pmic.202100161
(3) Yansu Wang, Lei Xu, Quan Zou, Chen Lin. prPred-DRLF: plant R protein predictor using deep representation learning features. Proteomics. 2021. DOI: 10.1002/pmic.202100161