An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Abstract

MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems’ prediction accuracy and reliability are frequently harmed because of the small data size, human factors’ limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model’s main advantage is the enhanced accuracy in plant miRNA–IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.

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

“…Despite not specifically indicating any disadvantages, the study successfully optimized the lncRNA sequence length, thus improving specificity. Moreover, an optimized ensemble deep learning model, which employs independent RNNs (IndRNNs) and CNNs, was developed by [ 77 ]. While the authors have not pointed out any disadvantages, their model’s advantages lie in the improved accuracy achieved through optimal hyperparameter tuning, suitable for large-scale data.…”

“…As we delve into the intricacies of lncRNA–miRNA associations, one can observe a clear trend toward leveraging advanced machine learning models for predicting these interactions, as evidenced by the studies summarized in Table 4 . A noteworthy focus has been on developing deep learning frameworks that offer both improved accuracy and applicability across various species [ 75 , 76 , 77 , 80 ].…”

“…BoT-Net, efficient lncRNA-miRNA interaction prediction using the bag of tricks-based neural network [72]; DeepWalk-LMI, inferring lncRNA-miRNA associations via comprehensive graph [73]; GCNCRF, predicting lncRNA-miRNA interactions using graph convolution and conditional random field [74]; MD-MLI, predicting lncRNA-miRNA interactions using multiple features and hierarchical deep learning [75]; ncRNAInter, a novel strategy using a graph neural network to discover lncRNA-miRNA interactions [76]; Optimized ensemble deep learning, predicting plant lncRNA-miRNA based on artificial gorilla troops algorithm [77]; PmliHFM, plant lncRNA-miRNA interaction prediction via hybrid feature mining network [78]; PmliPEMG, multi-level information enhancement and greedy fuzzy decision for plant lncRNA-miRNA interaction prediction [79]; preMLI, uncovering potential lncRNA-miRNA interactions through pre-training and deep feature mining [80].…”

Ten deep learning techniques to address small data problems with remote sensing

“…Nevertheless, ensemble DL models have the potential to harness the benefits of DL architecture as well as ensemble learning (e.g., to avoid overfitting). Previously, this method has been applied to predicting short-term traffic flow (Zhang and Xin, 2022), predicting plant miRNA-IncRNA (Hamdy et al, 2023), and identifying the drivers of vehicles by using Controller Area Network (CAN) bus data (Hu et al, n.d.). Ganaie et al also reviewed a variety of techniques that have been applied in different domains (Ganaie et al, 2022).…”