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Ahmed FR, Alsenany SA, Abdelaliem SMF, Deif MA. Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction. Sci Rep 2023; 13:20927. [PMID: 38017008 PMCID: PMC10684522 DOI: 10.1038/s41598-023-47837-8] [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: 09/02/2023] [Accepted: 11/19/2023] [Indexed: 11/30/2023] Open
Abstract
The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.
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Affiliation(s)
- Fatma Refaat Ahmed
- Department of Nursing, College of Health Sciences, University of Sharjah, Sharjah, UAE
- Critical Care and Emergency Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt
| | - Samira Ahmed Alsenany
- Department of Community Health Nursing, College of Nursing, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Sally Mohammed Farghaly Abdelaliem
- Department of Nursing Management and Education, College of Nursing, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
| | - Mohanad A Deif
- Department of Artificial Intelligence, College of Information Technology, Misr University for Science and Technology (MUST), 6th of October City, 12566, Egypt
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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Vilain M, Aris-Brosou S. Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations. Viruses 2023; 15:1226. [PMID: 37376526 DOI: 10.3390/v15061226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 06/29/2023] Open
Abstract
During the SARS-CoV-2 pandemic, much effort has been geared towards creating models to predict case numbers. These models typically rely on epidemiological data, and as such overlook viral genomic information, which could be assumed to improve predictions, as different variants show varying levels of virulence. To test this hypothesis, we implemented simple models to predict future case numbers based on the genomic sequences of the Alpha and Delta variants, which were co-circulating in Texas and Minnesota early during the pandemic. Sequences were encoded, matched with case numbers at a future time based on collection date, and used to train two algorithms: one based on random forests and one based on a feed-forward neural network. While prediction accuracies were ≥93%, explainability analyses showed that the models were not associating case numbers with mutations known to have an impact on virulence, but with individual variants. This work highlights the necessity of gaining a better understanding of the data used for training and of conducting explainability analysis to assess whether model predictions are misleading.
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Affiliation(s)
- Matthieu Vilain
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Stéphane Aris-Brosou
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images. Viruses 2022; 14:v14112386. [PMID: 36366485 PMCID: PMC9698148 DOI: 10.3390/v14112386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/23/2022] [Accepted: 10/24/2022] [Indexed: 01/31/2023] Open
Abstract
The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus's characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10-11 at 639 epoch, regression of -1.6 × 10-9, momentum gain (Mu) 1 × 10-9, and gradient value of 9.6852 × 10-8, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.
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Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6364102. [PMID: 36210968 PMCID: PMC9546660 DOI: 10.1155/2022/6364102] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/04/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022]
Abstract
Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.
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Pei Y, Guo Y, Wu T, Liang H. Quantifying the dynamic transmission of COVID-19 asymptomatic and symptomatic infections: Evidence from four Chinese regions. Front Public Health 2022; 10:925492. [PMID: 36249263 PMCID: PMC9557086 DOI: 10.3389/fpubh.2022.925492] [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: 04/21/2022] [Accepted: 09/07/2022] [Indexed: 01/24/2023] Open
Abstract
The dynamic transmission of asymptomatic and symptomatic COVID-19 infections is difficult to quantify because asymptomatic infections are not readily recognized or self-identified. To address this issue, we collected data on asymptomatic and symptomatic infections from four Chinese regions (Beijing, Dalian, Xinjiang, and Guangzhou). These data were considered reliable because the government had implemented large-scale multiple testing during the outbreak in the four regions. We modified the classical susceptible-exposure-infection-recovery model and combined it with mathematical tools to quantitatively analyze the number of infections caused by asymptomatic and symptomatic infections during dynamic transmission, respectively. The results indicated that the ratios of the total number of asymptomatic to symptomatic infections were 0.13:1, 0.48:1, 0.29:1, and 0.15:1, respectively, in the four regions. However, the ratio of the total number of infections caused by asymptomatic and symptomatic infections were 4.64:1, 6.21:1, 1.49:1, and 1.76:1, respectively. Furthermore, the present study describes the daily number of healthy people infected by symptomatic and asymptomatic transmission and the dynamic transmission process. Although there were fewer asymptomatic infections in the four aforementioned regions, their infectivity was found to be significantly higher, implying a greater need for timely screening and control of infections, particularly asymptomatic ones, to contain the spread of COVID-19.
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Affiliation(s)
- Yuanyuan Pei
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China,*Correspondence: Yuanyuan Pei
| | - Yi Guo
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China
| | - Tong Wu
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China
| | - Huiying Liang
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China,Medical Research Department, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China,Huiying Liang
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Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in Chinese Pregnant Women: A Prospective Cohort Study. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8948082. [PMID: 36147870 PMCID: PMC9489389 DOI: 10.1155/2022/8948082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner's inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628–0.648)) and specificity (0.683 (0.669–0.698)) and lowest error rate (0.362 (0.352–0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663–0.670)) and specificity (0.725 (0.721–0.729)) and decreased the error rate (0.333 (0.330–0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.
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Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques. SENSORS 2022; 22:s22103846. [PMID: 35632254 PMCID: PMC9144943 DOI: 10.3390/s22103846] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/17/2022] [Indexed: 02/04/2023]
Abstract
Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use.
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Dashti H, Dehzangi I, Bayati M, Breen J, Beheshti A, Lovell N, Rabiee HR, Alinejad-Rokny H. Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer. BMC Bioinformatics 2022; 23:138. [PMID: 35439935 PMCID: PMC9017053 DOI: 10.1186/s12859-022-04652-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 03/24/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Recent studies have observed causative mutations in susceptible genes related to colorectal cancer in 10 to 15% of the patients. This highlights the importance of identifying mutations for early detection of this cancer for more effective treatments among high risk individuals. Mutation is considered as the key point in cancer research. Many studies have performed cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes. However, to the best of our knowledge, combination of these features has never been used together for this task. This highlights the potential to introduce better and more inclusive subtype classification approaches using wider range of related features to enable biomarker discovery and thus inform drug development for CRC. RESULTS In this study, we develop a new pipeline based on a novel concept called 'gene-motif', which merges mutated gene information with tri-nucleotide motif of mutated sites, for colorectal cancer subtype identification. We apply our pipeline to the International Cancer Genome Consortium (ICGC) CRC samples and identify, for the first time, 3131 gene-motif combinations that are significantly mutated in 536 ICGC colorectal cancer samples. Using these features, we identify seven CRC subtypes with distinguishable phenotypes and biomarkers, including unique cancer related signaling pathways, in which for most of them targeted treatment options are currently available. Interestingly, we also identify several genes that are mutated in multiple subtypes but with unique sequence contexts. CONCLUSION Our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers. The new CRC subtypes presented in this study demonstrates distinguished phenotypic properties which can be effectively used to develop new treatments. By knowing the genes and phenotypes associated with the subtypes, a personalized treatment plan can be developed that considers the specific phenotypes associated with their genomic lesion.
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Affiliation(s)
- Hamed Dashti
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, 11365, Tehran, Iran
| | - Iman Dehzangi
- Center for Computational and Integrative Biology (CCIB), Rutgers University, Camden, NJ, 08102, USA
| | - Masroor Bayati
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, 11365, Tehran, Iran
| | - James Breen
- South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia.,Robinson Research Institute, University of Adelaide, Adelaide, SA, 5006, Australia.,Bioinformatics Hub, University of Adelaide, Adelaide, SA, 5006, Australia
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW, 2109, Australia
| | - Nigel Lovell
- Tyree Institute of Health Engineering and The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Hamid R Rabiee
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, 11365, Tehran, Iran.
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia. .,UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia. .,Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, 2109, Australia.
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Pi J, Jiao P, Zhang Y, Li J. MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network. Front Microbiol 2022; 13:819046. [PMID: 35464940 PMCID: PMC9021438 DOI: 10.3389/fmicb.2022.819046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/07/2022] [Indexed: 11/14/2022] Open
Abstract
Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insights into the mechanism of drug–virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on the graph convolutional networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an area under the curve (AUC) of 0.9726 and an area under the PR curve (AUPR) of 0.9112. In this case study, two drugs related to SARS-CoV-2 were successfully predicted and verified by the relevant literature. The data and code are open source and can be accessed from https://github.com/Pijiangsheng/MDGNN.
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Affiliation(s)
- Jiangsheng Pi
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Peishun Jiao
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Yang Zhang
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, China
- *Correspondence: Yang Zhang,
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
- Junyi Li,
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A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area. SUSTAINABILITY 2021. [DOI: 10.3390/su14010296] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.
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