1
|
Zhang ZY, Sun ZJ, Gao D, Hao YD, Lin H, Liu F. Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression. IET Syst Biol 2024. [PMID: 38530028 DOI: 10.1049/syb2.12090] [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: 11/20/2023] [Revised: 02/06/2024] [Accepted: 03/10/2024] [Indexed: 03/27/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.
Collapse
Affiliation(s)
- Zhao-Yue Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Zi-Jie Sun
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dong Gao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Duo Hao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fen Liu
- Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), Affiliated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China
| |
Collapse
|
2
|
Huang AA, Huang SY. Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population. PLoS One 2023; 18:e0288903. [PMID: 37992024 PMCID: PMC10664888 DOI: 10.1371/journal.pone.0288903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/05/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions. OBJECTIVE The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults. METHODS A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. RESULTS 7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%). CONCLUSION Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.
Collapse
Affiliation(s)
- Alexander A. Huang
- Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Samuel Y. Huang
- Virginia Commonwealth University School of Medicine, Richmond, VA, United States of America
| |
Collapse
|
3
|
Prasad SS, Deo RC, Salcedo-Sanz S, Downs NJ, Casillas-Pérez D, Parisi AV. Enhanced joint hybrid deep neural network explainable artificial intelligence model for 1-hr ahead solar ultraviolet index prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107737. [PMID: 37573641 DOI: 10.1016/j.cmpb.2023.107737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/16/2023] [Accepted: 07/27/2023] [Indexed: 08/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Exposure to solar ultraviolet (UV) radiation can cause malignant keratinocyte cancer and eye disease. Developing a user-friendly, portable, real-time solar UV alert system especially or wearable electronic mobile devices can help reduce the exposure to UV as a key measure for personal and occupational management of the UV risks. This research aims to design artificial intelligence-inspired early warning tool tailored for short-term forecasting of UV index (UVI) integrating satellite-derived and ground-based predictors for Australian hotspots receiving high UV exposures. The study further improves the trustworthiness of the newly designed tool using an explainable artificial intelligence approach. METHODS An enhanced joint hybrid explainable deep neural network model (called EJH-X-DNN) is constructed involving two phases of feature selection and hyperparameter tuning using Bayesian optimization. A comprehensive assessment of EJH-X- DNN is conducted with six other competing benchmarked models. The proposed model is explained locally and globally using robust model-agnostic explainable artificial intelligence frameworks such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley additive explanations (SHAP), and permutation feature importance (PFI). RESULTS The newly proposed model outperformed all benchmarked models for forecasting hourly horizons UVI, with correlation coefficients of 0.900, 0.960, 0.897, and 0.913, respectively, for Darwin, Alice Springs, Townsville, and Emerald hotspots. According to the combined local and global explainable model outcomes, the site-based results indicate that antecedent lagged memory of UVI and solar zenith angle are influential features. Predictions made by EJH-X-DNN model are strongly influenced by factors such as ozone effect, cloud conditions, and precipitation. CONCLUSION With its superiority and skillful interpretation, the UVI prediction system reaffirms its benefits for providing real-time UV alerts to mitigate risks of skin and eye health complications, reducing healthcare costs and contributing to outdoor exposure policy.
Collapse
Affiliation(s)
- Salvin S Prasad
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
| | - Ravinesh C Deo
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
| | - Sancho Salcedo-Sanz
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Madrid, Spain.
| | - Nathan J Downs
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - David Casillas-Pérez
- Department of Signal Processing and Communications, Universidad Rey Juan Carlos, Fuenlabrada, 28942, Madrid, Spain.
| | - Alfio V Parisi
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| |
Collapse
|
4
|
Liu B, Yang Z, Liu Q, Zhang Y, Ding H, Lai H, Li Q. Computational prediction of allergenic proteins based on multi-feature fusion. Front Genet 2023; 14:1294159. [PMID: 37928245 PMCID: PMC10622758 DOI: 10.3389/fgene.2023.1294159] [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: 09/15/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.
Collapse
Affiliation(s)
- Bin Liu
- Department of Anesthesiology, The Fourth People's Hospital of Sichuan Province, Chengdu, Sichuan, China
| | - Ziman Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Liu
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Ying Zhang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Hui Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyan Lai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Qun Li
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Research Center of Integrated Traditional Chinese and Western Medicine, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| |
Collapse
|
5
|
Joseph LP, Joseph EA, Prasad R. Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture. Comput Biol Med 2022; 151:106178. [PMID: 36306578 DOI: 10.1016/j.compbiomed.2022.106178] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 09/23/2022] [Accepted: 10/01/2022] [Indexed: 12/27/2022]
Abstract
Diabetes is a deadly chronic disease that occurs when the pancreas is not able to produce ample insulin or when the body cannot use insulin effectively. If undetected, it may lead to a host of health complications. Hence, accurate and explainable early-stage detection of diabetes is essential for the proper administration of treatment options in leading a healthy and productive life. For this, we developed an interpretable TabNet model tuned via Bayesian optimization (BO). To achieve model-specific interpretability, the attention mechanism of TabNet architecture was used, which offered the local and global model explanations on the influence of the attributes on the outcomes. The model was further explained locally and globally using more robust model-agnostic LIME and SHAP eXplainable Artificial Intelligence (XAI) tools. The proposed model outperformed all benchmarked models by obtaining high accuracy of 92.2% and 99.4% using the Pima Indians diabetes dataset (PIDD) and the early-stage diabetes risk prediction dataset (ESDRPD), respectively. Based on the XAI results, it was clear that the most influential attribute for diabetes classification using PIDD and ESDRPD were Insulin and Polyuria, respectively. The feature importance values registered for insulin was 0.301 (PIDD) and for polyuria 0.206 was registered (ESDRPD). The high accuracy and ancillary interpretability of our objective model is expected to increase end-users trust and confidence in early-stage detection of diabetes.
Collapse
Affiliation(s)
- Lionel P Joseph
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia
| | - Erica A Joseph
- Umanand Prasad School of Medicine and Health Sciences, The University of Fiji, Saweni, Lautoka, Fiji
| | - Ramendra Prasad
- Department of Science, School of Science and Technology, The University of Fiji, Saweni, Lautoka, Fiji.
| |
Collapse
|
6
|
Dutta A, Hasan MK, Ahmad M, Awal MA, Islam MA, Masud M, Meshref H. Early Prediction of Diabetes Using an Ensemble of Machine Learning Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912378. [PMID: 36231678 PMCID: PMC9566114 DOI: 10.3390/ijerph191912378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/20/2022] [Accepted: 09/24/2022] [Indexed: 05/15/2023]
Abstract
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.
Collapse
Affiliation(s)
- Aishwariya Dutta
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
- Department of Biomedical Engineering (BME), Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka 1216, Bangladesh
| | - Md. Kamrul Hasan
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Mohiuddin Ahmad
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Md. Abdul Awal
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
- Electronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, Bangladesh
- Correspondence:
| | | | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Hossam Meshref
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| |
Collapse
|
7
|
Smart ECG Biosensor Design with an Improved ANN Performance Based on the Taguchi Optimizer. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9090482. [PMID: 36135028 PMCID: PMC9495665 DOI: 10.3390/bioengineering9090482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022]
Abstract
This paper aims to design a smart biosensor to predict electrocardiogram (ECG) signals in a specific auscultation site from other ECG signals measured from other measurement sites. The proposed design is based on a hybrid architecture using the Artificial Neural Networks (ANNs) model and Taguchi optimizer to avoid the ANN issues related to hyperparameters and to improve its accuracy. The proposed approach aims to optimize the number and type of inputs to be considered for the ANN model. Indeed, different combinations are considered in order to find the optimal input combination for the best prediction quality. By identifying the factors that influence a model’s prediction and their degree of importance via the modified Taguchi optimizer, the developed biosensor improves the prediction accuracy of ECG signals collected from different auscultation sites compared to the ANN-based biosensor. Based on an actual database, the simulation results show that this improvement is significant; it can reach more than 94% accuracy.
Collapse
|
8
|
Raihan M, Hassan MM, Hasan T, Bulbul AAM, Hasan MK, Hossain MS, Roy DS, Awal MA. Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage. Bioengineering (Basel) 2022; 9:bioengineering9070281. [PMID: 35877332 PMCID: PMC9311761 DOI: 10.3390/bioengineering9070281] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/15/2022] [Accepted: 06/23/2022] [Indexed: 01/03/2023] Open
Abstract
COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has 2.82% positive and 97.18% negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved 97.91%, 97.81%, and 73.37%, respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users’ questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository.
Collapse
Affiliation(s)
- M. Raihan
- Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh; (M.R.); (M.M.H.); (T.H.)
| | - Md. Mehedi Hassan
- Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh; (M.R.); (M.M.H.); (T.H.)
| | - Towhid Hasan
- Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh; (M.R.); (M.M.H.); (T.H.)
| | - Abdullah Al-Mamun Bulbul
- Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh;
| | - Md. Kamrul Hasan
- Department of Electrical & Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh;
| | - Md. Shahadat Hossain
- Department of Quantitative Sciences, International University of Business Agriculture and Technology, Dhaka 1230, Bangladesh;
| | - Dipa Shuvo Roy
- Department of Information Science & Engineering, Visvesvaraya Technological University, Jnana Sangama, VTU Main Rd., Machhe, Belagavi 590018, India;
| | - Md. Abdul Awal
- Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh;
- Correspondence: ; Tel.: +880-178-619-6913
| |
Collapse
|