BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

International Journal of Molecular Sciences
Zhao-Hui ZhanHai-Cheng Yi

Abstract

The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approac...Continue Reading

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Citations

Oct 3, 2020·Molecular Genetics and Genomics : MGG·Lin ZhongQi Zhao
Dec 24, 2019·Briefings in Bioinformatics·Qiang ShiZhidong Xue
Dec 4, 2020·Non-coding RNA·Tanvir AlamSebastian Schmeier
Mar 18, 2021·Scientific Reports·Neda Emami, Reza Ferdousi

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Methods Mentioned

BETA
interaction prediction
feature extraction

Software Mentioned

RPI
learn
IPMiner ( Interaction Pattern Miner )
BLAST
Pred
Scikit
BGFE
PSI
keras

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