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Mubarak AS, Ameen ZS, Hassan AS, Ozsahin DU. Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction. Sci Rep 2024; 14:10375. [PMID: 38710737 DOI: 10.1038/s41598-024-59291-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 04/09/2024] [Indexed: 05/08/2024] Open
Abstract
Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.
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Affiliation(s)
- Auwalu Saleh Mubarak
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
- Department of Electrical Engineering, Aliko Dangote University of Science and Technology, Wudil, Kano, Nigeria
| | - Zubaida Said Ameen
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
- Department of Biochemistry, Yusuf Maitama Sule University, Kano, Nigeria
| | - Abdurrahman Shuaibu Hassan
- Department of Electrical Electronics and Automation Systems Engineering, Kampala International University, Kampala, Uganda.
| | - Dilber Uzun Ozsahin
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey.
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, UAE.
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, UAE.
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Ozsahin DU, Ameen ZS, Hassan AS, Mubarak AS. Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis. Sci Rep 2024; 14:6737. [PMID: 38509174 PMCID: PMC10954760 DOI: 10.1038/s41598-024-55762-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, UAE
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, UAE
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
| | - Zubaida Said Ameen
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
- Department of Biochemistry, Yusuf Maitama Sule University, Kano, Nigeria
| | - Abdurrahman Shuaibu Hassan
- Department of Electrical Electronics and Automation Systems Engineering, Kampala International University, Kampala, Uganda.
| | - Auwalu Saleh Mubarak
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
- Department of Electrical Engineering, Aliko Dangote University of Science and Technology, Wudil, Kano, Nigeria
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Abubakar H, Al-Turjman F, Ameen ZS, Mubarak AS, Altrjman C. A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images. Heliyon 2024; 10:e26939. [PMID: 38463848 PMCID: PMC10920381 DOI: 10.1016/j.heliyon.2024.e26939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/12/2024] Open
Abstract
COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate. Owing to the COVID-19 pandemic, traditional diagnostic methods such as reverse-transcription polymerase chain reaction (RT-PCR) are ineffective for diagnosis. Medical imaging is among the most effective techniques of respiratory disorders detection through machine learning and deep learning. However, conventional machine learning methods depend on extracted and engineered features, whereby the optimum features influence the classifier's performance. In this study, Histogram of Oriented Gradient (HOG) and eight deep learning models were utilized for feature extraction while K-Nearest Neighbour (KNN) and Support Vector Machines (SVM) were used for classification. A combined feature of HOG and deep learning feature was proposed to improve the performance of the classifiers. VGG-16 + HOG achieved 99.4 overall accuracy with SVM. This indicates that our proposed concatenated feature can enhance the SVM classifier's performance in COVID-19 detection.
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Affiliation(s)
- Hassana Abubakar
- Biomedical Engineering Department, Faculty of Engineering, Near East University, Mersin 10, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
| | - Zubaida S. Ameen
- Operational Research Center in Healthcare, Near East University, Mersin 10, Turkey
| | - Auwalu S. Mubarak
- Operational Research Center in Healthcare, Near East University, Mersin 10, Turkey
| | - Chadi Altrjman
- Waterloo University, 200 University Avenue West. Waterloo, ON, Canada
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Prince R, Niu Z, Khan ZY, Emmanuel M, Patrick N. COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms. BMC Bioinformatics 2024; 25:28. [PMID: 38233764 PMCID: PMC10792799 DOI: 10.1186/s12859-023-05427-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 07/20/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND COVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time. RESULTS In this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination-Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient. CONCLUSION Our proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript's availability of the data and materials under the declaration section for access.
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Affiliation(s)
- Rukundo Prince
- Department of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Zhendong Niu
- Department of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Zahid Younas Khan
- Computer Science and Information Technology, University of Azad Jammu and Kashmir, Kashmir, Pakistan
| | - Masabo Emmanuel
- Software Engineering, African Center of Excellence in Data Science(ACE-DS), and the African Center of Excellence in Internet of Things(ACEIoT), University of Rwanda, Kigali, Rwanda
| | - Niyishaka Patrick
- Computer and Information Sciences, University of Hyderabad, Hyderabad, India
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Khan A, Khan SH, Saif M, Batool A, Sohail A, Waleed Khan M. A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2165724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Asifullah Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
- Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
| | - Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan
| | - Mahrukh Saif
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Asiya Batool
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Science, Faculty of Computing & Artificial Intelligence, Air University, Islamabad, Pakistan
| | - Muhammad Waleed Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Mechanical and Aerospace Engineering, Columbus, OH, USA
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Mubarak AS, Ameen ZS, Al-Turjman F. Effect of Gaussian filtered images on Mask RCNN in detection and segmentation of potholes in smart cities. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:283-295. [PMID: 36650766 DOI: 10.3934/mbe.2023013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Accidents have contributed a lot to the loss of lives of motorists and serious damage to vehicles around the globe. Potholes are the major cause of these accidents. It is very important to build a model that will help in recognizing these potholes on vehicles. Several object detection models based on deep learning and computer vision were developed to detect these potholes. It is very important to develop a lightweight model with high accuracy and detection speed. In this study, we employed a Mask RCNN model with ResNet-50 and MobileNetv1 as the backbone to improve detection, and also compared the performance of the proposed Mask RCNN based on original training images and the images that were filtered using a Gaussian smoothing filter. It was observed that the ResNet trained on Gaussian filtered images outperformed all the employed models.
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Affiliation(s)
- Auwalu Saleh Mubarak
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Electrical Engineering Dept., Kano University of Science and Technology, Wudil, Kano, Nigeria
| | - Zubaida Said Ameen
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Biochemistry Dept., Maitama Sule University, Kano, Nigeria
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Kyrenia, Mersin 10, Turkey
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