Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction
The accurate prediction of the host of influenza viruses is a significant challenge in bioinformatics, as it is crucial for understanding viral transmission dynamics and host-virus interactions. This research • Introduces a novel approach for predicting the host of influenza viruses by leveraging pr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125001657 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850121312826032128 |
|---|---|
| author | Shweta Ashish Koparde Sonali Kothari Sharad Adsure Kapil Netaji Vhatkar Vinod V. Kimbahune |
| author_facet | Shweta Ashish Koparde Sonali Kothari Sharad Adsure Kapil Netaji Vhatkar Vinod V. Kimbahune |
| author_sort | Shweta Ashish Koparde |
| collection | DOAJ |
| description | The accurate prediction of the host of influenza viruses is a significant challenge in bioinformatics, as it is crucial for understanding viral transmission dynamics and host-virus interactions. This research • Introduces a novel approach for predicting the host of influenza viruses by leveraging protein sequences. • Extraction of features, including sequence length, Amino Acid Composition (AAC), Dipeptide Composition (DPC), Tripeptide Composition (TPC), aromaticity, secondary structure fraction, and entropy from protein sequence. • Addresses the data imbalance and improves model generalization, the oversampling technique is applied for data augmentation.The prediction model employs a Deep Recurrent Neural Network (DRNN) optimized by Fractional Addax Optimization 34 Algorithm (FAOA), a hybrid of Addax Optimization Algorithm (AOA) and Fractional Concept (FC), designed to perform 35 influenza virus host prediction. The model's performance is evaluated using metrics, such as Matthews's Correlation 36 Coefficient (MCC), F1-Score, and Mean Squared Error (MSE). Experimental results demonstrate that the DRNN_FAOA 37 model significantly outperforms existing methods, achieving the highest MCC of 0.937, F1-Score of 0.917, and the 38 lowest MSE of 0.038. The proposed DRNN_FAOA model's ability to accurately predict influenza virus hosts suggests its 39 potential as a robust model in virus-host prediction. |
| format | Article |
| id | doaj-art-4d122794fbab404996ffa3503f74ffb7 |
| institution | OA Journals |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-4d122794fbab404996ffa3503f74ffb72025-08-20T02:35:07ZengElsevierMethodsX2215-01612025-06-011410331910.1016/j.mex.2025.103319Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host predictionShweta Ashish Koparde0Sonali Kothari1Sharad Adsure2Kapil Netaji Vhatkar3Vinod V. Kimbahune4Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, IndiaSymbiosis Institute of Technology – Pune Campus, Symbiosis International (Deemed University), Pune, India; Corresponding author.Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, IndiaDepartment of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, IndiaDepartment of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, IndiaThe accurate prediction of the host of influenza viruses is a significant challenge in bioinformatics, as it is crucial for understanding viral transmission dynamics and host-virus interactions. This research • Introduces a novel approach for predicting the host of influenza viruses by leveraging protein sequences. • Extraction of features, including sequence length, Amino Acid Composition (AAC), Dipeptide Composition (DPC), Tripeptide Composition (TPC), aromaticity, secondary structure fraction, and entropy from protein sequence. • Addresses the data imbalance and improves model generalization, the oversampling technique is applied for data augmentation.The prediction model employs a Deep Recurrent Neural Network (DRNN) optimized by Fractional Addax Optimization 34 Algorithm (FAOA), a hybrid of Addax Optimization Algorithm (AOA) and Fractional Concept (FC), designed to perform 35 influenza virus host prediction. The model's performance is evaluated using metrics, such as Matthews's Correlation 36 Coefficient (MCC), F1-Score, and Mean Squared Error (MSE). Experimental results demonstrate that the DRNN_FAOA 37 model significantly outperforms existing methods, achieving the highest MCC of 0.937, F1-Score of 0.917, and the 38 lowest MSE of 0.038. The proposed DRNN_FAOA model's ability to accurately predict influenza virus hosts suggests its 39 potential as a robust model in virus-host prediction.http://www.sciencedirect.com/science/article/pii/S2215016125001657Fractional Addax Optimization Algorithm |
| spellingShingle | Shweta Ashish Koparde Sonali Kothari Sharad Adsure Kapil Netaji Vhatkar Vinod V. Kimbahune Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction MethodsX Fractional Addax Optimization Algorithm |
| title | Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction |
| title_full | Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction |
| title_fullStr | Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction |
| title_full_unstemmed | Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction |
| title_short | Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction |
| title_sort | deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction |
| topic | Fractional Addax Optimization Algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125001657 |
| work_keys_str_mv | AT shwetaashishkoparde deeprecurrentneuralnetworkwithfractionaladdaxoptimizationalgorithmforinfluenzavirushostprediction AT sonalikothari deeprecurrentneuralnetworkwithfractionaladdaxoptimizationalgorithmforinfluenzavirushostprediction AT sharadadsure deeprecurrentneuralnetworkwithfractionaladdaxoptimizationalgorithmforinfluenzavirushostprediction AT kapilnetajivhatkar deeprecurrentneuralnetworkwithfractionaladdaxoptimizationalgorithmforinfluenzavirushostprediction AT vinodvkimbahune deeprecurrentneuralnetworkwithfractionaladdaxoptimizationalgorithmforinfluenzavirushostprediction |