RPIPCM: A deep network model for predicting lncRNA-protein interaction based on sequence feature encoding

LncRNA-protein interactionplays an important regulatory role in biological processes. In this paper, the proposed RPIPCM based on a novel deep network model uses the sequence feature encoding of both RNA and protein to predict lncRNA-protein interactions (LPIs). A negative sampling of sliding window method is proposed for solving the problem of unbalanced between positive and negative samples. The proposed negative sampling method is effective and helpful to solve the problem of data imbalance in the existing LPIs research by comparative experiments. Experimental results also show that the proposed sequence feature encoding method has good performance in predicting LPIs for different datasets of different sizes and types. In the RPI488 dataset related to animal, compared with the direct original sequence encoding model, the accuracy of sequence feature encoding model increased by 1.02%, the recall increased by 4.08%, and the value of MCC increased by 1.67%. In the case of the plant dataset ATH948, the sequence feature-based encoding demonstrated a 1.58% higher accuracy, a 1.53% higher recall, a 1.62% higher specificity, a 1.62% higher precision, and a 3.16% higher value of MCC compared to the direct original sequence-based encoding. Compared with the latest prediction work in the ZEA22133 dataset, RPIPCM is shown to be more effective with the accuracy increased by 2.23%, the recall increased by 1.78%, the specificity increased by 2.67%, the precision increased by 2.52%, and the value of MCC increased by 4.43%, which also proves the effectiveness and robustness of RPIPCM. In conclusion, RPIPCM of deep network model based on sequence feature encoding can automatically mine the hidden feature information of the sequence in the lncRNA-protein interaction without relying on external features or prior biomedical knowledge, and its low cost and high efficiency can provide a reference for biomedical researchers.