Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

Abstract

Reliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.

Deep Learning Model to Detect Diabetes Mellitus Based on DNA Sequence

“…The pooling layer works separately on each feature map produced from the convolution layer to create a new set of pooled feature maps. The final output layer is a fully connected neural network layer that produces the output based on the activation function [2].…”

“…DL is an evolution of ML, recently considered an attractive solution for genome classification and prediction problems. For instance, the Deep Neural Network (DNN) structure comprises multiple layers of non-linear transformations, making it more scalable and flexible in dealing with massive amounts of data and identifying the complex patterns in the feature-rich datasets [2]. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Restricted Boltzmann Machine (RBM), and Long Short-Term Memory (LSTM) are considered the most common architectures of DNN [3].…”

Polymeric Carriers Designed for Encapsulation of Essential Oils with Biological Activity

“…As Noha E. El-Attar’s article mentioned, the bioactivity of EOs as dynamic blends depends and varies according to their chemical constitution and structure [ 179 ]. The research team proposed the development of machine learning-based computational models, namely Multiclass Neural Network (MNN), and Convolutional Neural Network (CNN), as deep learning techniques, to categorize and predict the biological activities of EOs based on their chemical construction variations, without recourse to the in vitro experiments.…”

“…The following are available online at https://www.mdpi.com/article/ 10.3390/pharmaceutics13050631/s1, Table S1: The table presenting the polymeric matrices utilized for essential oils (EOs) encapsulation was made based on the documentation performed for the realization of the review and highlights the EOs used and the resulted products with specific biological activity, together with the corresponding reference used. References [55,56,64,72,73,81,82,85,87,113,116,118,119,[121][122][123]125,[128][129][130][131][132][133]135,136,145,[147][148][149][151][152][153][154][155][156][157][158][159]162,164,169,179] are cited in supplementary materials.…”

Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites

“…In the literature, previous studies using these methods have been applied to EOs, which have been useful to select antiviral and low toxic samples [3], antibiofilm formation by Staphylococcus aureus [16,29], S. epidermidis [16] and Pseudomonas aeruginosa [18,30], as different biological activities such as antiviral, anthelminthic, anti-inflammatory, anticancer, antioxidant, antimicrobial, antifungal and cytotoxic activity [31]. However, the application on EOs with antiprotozoal activity has been scarcely documented.…”