An Optimized Technique for RNA Prediction Based on Neural Network

Abstract

Pathway reconstruction, which remains a primary goal for many investigations, requires accurate inference of gene interactions and causality. Non-coding RNA (ncRNA) is studied because it has a significant regulatory role in many plant and animal life activities, but interacting micro-RNA (miRNA) and long non-coding RNA (lncRNA) are more important. Their interactions not only aid in the in-depth research of genes' biological roles, but also bring new ideas for illness detection and therapy, as well as plant genetic breeding. Biological investigations and classical machine learning methods are now used to predict miRNA-lncRNA interactions. Because biological identification is expensive and time-consuming, machine learning requires too much manual intervention, and the feature extraction process is difficult. This research presents a deep learning model that combines the advantages of convolutional neural networks (CNN) and bidirectional long short-term memory networks (Bi-LSTM). It not only takes into account the connection of information between sequences and incorporates contextual data, but it also thoroughly extracts the sequence data's features. On the corn data set, cross-checking is used to evaluate the model's performance, and it is compared to classical machine learning. To acquire a superior classification effect, the proposed strategy was compared to a single model. Additionally, the potato and wheat data sets were utilized to evaluate the model, with accuracy rates of 95% and 93%, respectively, indicating that the model had strong generalization capacity.

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

“…A further significant facet of lncRNA research that has reaped the benefits of deep learning involves the identification and prediction of characteristics inherent to lncRNAs [ 88 , 100 ]. By leveraging the ability of deep learning models to learn complex representations from data, researchers have made inroads in understanding the fundamental properties that define lncRNAs.…”

“…For instance, multiple studies proposed CNN structures to handle these challenges [ 82 , 83 , 89 ]. Additionally, LSTM structures were commonly employed for the prediction of lncRNA characteristics [ 88 ].…”

“…Class similarity network, identifying lncRNAs using relationships among samples [81]; DeepLncPro, CNN for identifying lncRNA promoters [82]; DeepPlnc, high accuracy plant lncRNA identification using bimodal CNN [83]; Genome-wide analysis, exploring features related to human lncRNA stability [84]; LncDLSM, lncRNA identification using the deep learning-based sequence model [85]; LncReader, identifying dual-functional lncRNAs using multi-head self-attention [86]; lncIBTP, predicting interaction biomolecule type for a given lncRNA using ensemble deep learning [87]; RNA prediction based on neural network integration of CNN and Bi-LSTM [88]; Xlnc1DCNN, interpretable deep learning model, lncRNA identification using 1D CNN [89].…”

“…These computational tools are crucial in enabling more accurate, efficient, and scalable analyses of lncRNA data. One remarkable trend observed is the utilization of CNN and LSTM in deep learning models for the prediction and classification of lncRNA [82,83,88,89]. These sophisticated tools offer high accuracy rates and demonstrate robust performance across various datasets.…”