Open Access

ARTICLE

An Optimized Technique for RNA Prediction Based on Neural Network

by Ahmad Ali AlZubi^*, Jazem Mutared Alanazi

Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia

* Corresponding Author: Ahmad Ali AlZubi. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3599-3611. https://doi.org/10.32604/iasc.2023.027913

Received 27 January 2022; Accepted 07 March 2022; Issue published 17 August 2022

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.

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Keywords

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