1
|
Zheng B, Li Y, Xiong G. Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease. Front Genet 2024; 15:1351774. [PMID: 38495669 PMCID: PMC10941628 DOI: 10.3389/fgene.2024.1351774] [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: 12/08/2023] [Accepted: 02/20/2024] [Indexed: 03/19/2024] Open
Abstract
Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.
Collapse
Affiliation(s)
- Biwei Zheng
- Department of Cardiology, Dongguan Hospital of Integrated Chinese and Western Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Dongguan, China
| | - Yujing Li
- Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
- Beijing University of Chinese Medicine Shenzhen Hospital (Longgang), Shenzhen, China
| | - Guoliang Xiong
- Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| |
Collapse
|
2
|
Xu X, Li J, Zhu Z, Zhao L, Wang H, Song C, Chen Y, Zhao Q, Yang J, Pei Y. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis. Bioengineering (Basel) 2024; 11:219. [PMID: 38534493 DOI: 10.3390/bioengineering11030219] [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: 12/29/2023] [Revised: 02/15/2024] [Accepted: 02/21/2024] [Indexed: 03/28/2024] Open
Abstract
Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.
Collapse
Affiliation(s)
- Xi Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Zhichao Zhu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Linna Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Huina Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Changwei Song
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Yining Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Qing Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jijiang Yang
- Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
| | - Yan Pei
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
| |
Collapse
|
3
|
Liu C, Pan F, Sun Z, Chen Z, Wang J. Exploring the pathogenesis and key genes associated of acute myocardial infarction complicated with Alzheimer's disease. Sci Rep 2024; 14:1449. [PMID: 38228864 PMCID: PMC10791667 DOI: 10.1038/s41598-024-52094-4] [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: 09/03/2023] [Accepted: 01/13/2024] [Indexed: 01/18/2024] Open
Abstract
Despite mounting evidence linking Acute Myocardial Infarction (AMI) to Alzheimer's disease (AD), the shared mechanism of these two conditions' occurrence remains unclear. This research aims to delve deeper into the molecular process of the occurrence of the two diseases. We retrieved the gene expression profiles of AD (GSE5281) and AMI (GSE66360) from the Gene Expression Omnibus database. Then, a total of 22 common differentially expressed genes (DEGs) including one downregulated gene and 21 upregulated genes were chosen for further analysis. Following the discovery of the common DEGs between AMI and AD, we performed protein-protein interaction analysis and hub gene identification analysis. Next, ten important hub genes were identified. Additionally, the key genes were identified by the least absolute shrinkage and selection operator and support vector machine-recursive feature elimination and multivariable logistic regression analysis. The BCL6 was identified to be the most connected with AMI and AD. Finally, the BCL6 gene was validated in the GSE40680 (AMI) and GSE122063 (AD) datasets. Our research indicates that AMI and AD share a comparable pathophysiology. The Hub genes, especially BCL6, were essential in developing AMI and AD. In addition, these hub genes and shared pathways can offer fresh perspectives for additional mechanism investigation.
Collapse
Affiliation(s)
- Chaosheng Liu
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Fuzhi Pan
- Department of Medical Image Science, Liaoning Cancer Hospital, Shenyang, Liaoning, China
| | - Zhiyu Sun
- Department of Cardiology, Dalian Friendship Hospital, Dalian, Liaoning, China
| | - Ziyu Chen
- Department of Gynecology and Obstetrics, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
| | - Junjie Wang
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
| |
Collapse
|
4
|
DeGroat W, Abdelhalim H, Patel K, Mendhe D, Zeeshan S, Ahmed Z. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Sci Rep 2024; 14:1. [PMID: 38167627 PMCID: PMC10762256 DOI: 10.1038/s41598-023-50600-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.
Collapse
Affiliation(s)
- William DeGroat
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Habiba Abdelhalim
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Kush Patel
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Dinesh Mendhe
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Zeeshan Ahmed
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA.
- Department of Medicine/Cardiovascular Disease and Hypertension, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
| |
Collapse
|