Identifying anti-tumor heat shock proteins based on evolutionary information using deep learning method

Heat shock proteins (HSPs) belong to stress proteins. The functions of HSPs are mainly reflected in three aspects: molecular chaperones, regulation of apoptosis and immune responses. Recent studies have shown that there is a certain correlation between HSPs and tumor cell. HSPs are participated in the invasion, proliferation and metastasis of tumor cells. Therefore, developing an accurate model for identification anti-tumor HSPs is a key step to understand molecular functions of HSPs and human tumor diseases. In this study, we propose using deep learning methods to identify anti-tumor HSPs. To seek out the optimal model for the dataset, several hyper-parameters are optimized according to the results of 10-fold cross-validation. Finally, the performance of the proposed model is further determined through an independent dataset. The experimental results indicated that the proposed model could classify anti-tumor HSPs with accuracy (ACC) of 93.76%, sensitivity (SN) of 92.80%, specificity (SP) of 93.33%, and Matthew's correlation coefficient (MCC) of 86.39% on the 10-fold cross-validation. Compared with other deep learing methods, using convolutional neural network (CNN) can achieve a significant improvement for identifying of anti-tumor HSPs.