mlmip / ldnfsgb

LDNFSGB:Predicting lncRNA-disease associations using network feature similarity and gradient boosting

First, we construct a feature vector to extract similarity information of different lncRNAs and diseases. Then, an autoencoder is used to reduce the dimensionality of the feature vector. Finally, the gradientboosting classifier is used to predict lncRNA-disease associations.

#Autoencoder.py This part is mainly to reduce the dimensionality of the sample features.

#Gradientboosting.py It is mainly used for prediction of lncRNA-disease association. At the same time, this part provides the performance evaluation of the model under 10-fold CV including AUC, accuracy, sensitive, precision, specificity and ROC curve drawing.

The original datasets used in this study are available from the corresponding references.

References Zhang, Y., Ye, F., Xiong, D. et al. LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting. BMC Bioinformatics 21, 377 (2020). https://doi.org/10.1186/s12859-020-03721-0

ldnfsgb's Issues

can you share the data of this code?thank you

呃呃

就这本科生大作业水平的代码也敢在论文里贴链接？屁创新没有，代码也不敢给全，恐怕自己都不知道数据要怎么跑，直接把降维的特征塞模型里边就能fit出0.93的AUC？你是真的牛批，居然直接就把特征扔进去，属实难绷。