RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision

Abstract

<abstract><p>Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions. The RLF-LPI of the residual LSTM autoencoder module with fusion attention mechanism can extract the potential representation of features and capture the dependencies between sequences and structures by k-mer method. Finally, the relationship between lncRNA and protein is learned through the method of fuzzy decision. The experimental results show that the ACC of RLF-LPI is 0.912 on ATH948 dataset and 0.921 on ZEA22133 dataset. Thus, it is demonstrated that our proposed method performed better in predicting lncRNA-protein interaction than other methods.</p></abstract>

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

“…In addition to predicting lncRNA-disease associations, deep learning models have also been used to predict lncRNA-protein interactions. Song et al [84] presented an ensemble learning framework, RLF-LPI, for predicting lncRNA-protein interactions. Wekesa et al [85] developed a graph representation learning method, GPLPI, for predicting plant lncRNA-protein interactions (LPIs) from sequence and structural information.…”

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

“…The third group is characterized by models that use deep learning frameworks with different architectures or hybrid approaches. These include DeepLPI [ 60 ], DFRPI [ 61 ], LPI–deepGBDT [ 66 ], LPI–DLDN [ 67 ], LPI–HyADBS [ 68 ], and RLF–LPI [ 71 ]. These models demonstrated high performance due to their unique approaches.…”

“…Secondly, the recent trend of employing ensemble learning and hybrid frameworks is noticeable in studies, such as in the works by Zhou et al [ 68 ] and Song et al [ 71 ]. These studies capitalize on the strength of diverse learning models, making the prediction of lncRNA–protein interactions more robust and less prone to overfitting.…”

“…LGFC-CNN, using deep learning with feature combination [64]; LPI-CSFFR, CNN-based lncRNA-protein interaction prediction with serial fusion and feature reuse [65]; LPI-deepGBDT, gradient boosting decision trees-based lncRNA-protein interaction identification [66]; LPI-DLDN, dual-net neural architecture for lncRNA-protein interactions prediction [67]; LPI-HyADBS, hybrid framework with DNN, XGBoost, SVM for lncRNA-protein interaction [68]; LPICGAE, predicting lncRNA-protein interactions using combined graph autoencoders [69]; PRPI-SC, ensemble deep learning for plant lncRNA-protein interactions prediction [70]; RLF-LPI, ensemble learning framework with residual LSTM and fusion attention [71].…”