1
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Fang F, Sun Y. Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network. Comput Biol Med 2024; 175:108371. [PMID: 38691916 DOI: 10.1016/j.compbiomed.2024.108371] [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: 01/31/2024] [Revised: 03/13/2024] [Accepted: 03/24/2024] [Indexed: 05/03/2024]
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, with numerous approaches having identified genes linked to its development, diagnosis and prognosis. Despite genome-wide association analysis and gene knockout experiments confirming some genes associated with SLE, there are still numerous potential genes yet to be discovered. The search for relevant genes through biological experiments entails significant financial and human resources. With the advancement of computational technologies like deep learning, we aim to identify SLE-related genes through deep learning methods, thereby narrowing down the scope for biological experimentation. This study introduces SLEDL, a deep learning-based approach that leverages DNN and graph neural networks to effectively identify SLE-related genes by capturing relevant features in the gene interaction network. The above steps transform the identification of SLE related genes into a binary classification problem, ultimately solved through a fully connected layer. The results demonstrate the superiority of SLEDL, achieving higher AUC (0.7274) and AUPR (0.7599), further validated through case studies.
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Affiliation(s)
- Fang Fang
- Department of Rheumatology and Immunology, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yizhou Sun
- Department of Ophthalmology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
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2
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Liu L, Jia R, Hou R, Huang C. Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state. Methods 2024; 226:151-160. [PMID: 38670416 DOI: 10.1016/j.ymeth.2024.04.014] [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: 03/02/2024] [Revised: 04/02/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.
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Affiliation(s)
- Li Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.
| | - Ranran Jia
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China.
| | - Rui Hou
- College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010051, China.
| | - Chengbing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba 623002, China.
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3
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Shi J, Chen Y, Wang Y. Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles. Comput Biol Med 2024; 175:108496. [PMID: 38657466 DOI: 10.1016/j.compbiomed.2024.108496] [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: 03/09/2024] [Revised: 04/14/2024] [Accepted: 04/21/2024] [Indexed: 04/26/2024]
Abstract
Distant metastasis of cancer is a significant contributor to cancer-related complications, and early identification of unidentified stomach adenocarcinoma is crucial for a positive prognosis. Changes inDNA methylation are being increasingly recognized as a crucial factor in predicting cancer progression. Within this research, we developed machine learning and deep learning models for distinguishing distant metastasis in samples of stomach adenocarcinoma based on DNA methylation profile. Employing deep neural networks (DNN), support vector machines (SVM), random forest (RF), Naive Bayes (NB) and decision tree (DT), and models for forecasting distant metastasis in stomach adenocarcinoma. The results show that the performance of DNN is better than that of other models, AUC and AUPR achieving 99.9 % and 99.5 % respectively. Additionally, a weighted random sampling technique was utilized to address the issue of imbalanced datasets, enabling the identification of crucial methylation markers associated with functionally significant genes in stomach distant metastasis tumors with greater performance.
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Affiliation(s)
- Jing Shi
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Ying Chen
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Ying Wang
- Department of Endoscopy, The First Hospital of China Medical University, Shenyang, China.
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4
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Wei W, Yue D. CoGSPro-net:A graph neural network based on protein-protein interaction for classifying lung cancer-relatrd proteins. Comput Biol Med 2024; 172:108251. [PMID: 38508055 DOI: 10.1016/j.compbiomed.2024.108251] [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: 01/21/2024] [Revised: 02/16/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
This paper proposes a deep learning algorithm named CoGSPro for classifying lung cancer-related proteins. CoGSPro combines graph neural networks and attention mechanisms to extract key features from protein data and accurately classify proteins. It utilizes large-scale protein expression datasets to train and validate the model, enabling it to identify subtle patterns related to lung cancer. CoGSPro integrates protein-protein interaction network information to improve its predictive accuracy. The experimental results indicate that CoGSPro achieves cutting-edge performance, attaining an accuracy of 96.60% in the classification of lung cancer proteins, surpassing other baseline methods. Additionally, CoGSPro has uncovered new biomarkers for lung cancer, offering potential targets for early detection and treatment.
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Affiliation(s)
- Wei Wei
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Lung Cancer Center, Tianjin, China
| | - Dongsheng Yue
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Lung Cancer Center, Tianjin, China.
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Wang Y, Du Y. Graph neural network model GGDisnet for identifying genes in gastrointestinal cancer and single-cell analysis. Comput Biol Med 2024; 172:108285. [PMID: 38503088 DOI: 10.1016/j.compbiomed.2024.108285] [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: 01/17/2024] [Revised: 02/22/2024] [Accepted: 03/12/2024] [Indexed: 03/21/2024]
Abstract
Gastrointestinal cancer, a highly prevalent form of cancer, has been the subject of extensive research resulting in the identification of numerous pathogenic genes. However, validation and exploration of these findings often require traditional biological experiments, which are time-consuming and limit the ability to make extensive assessments promptly. To address this challenge, this paper introduces GGDisnet, a novel model for identifying genes associated with gastrointestinal cancer. GGDisnet efficiently screens human genes, providing a set of genes with a high correlation to gastrointestinal cancer for reference. Comparative analysis with other models demonstrates GGDisnet's superior performance. Furthermore, we conducted enrichment and single-cell analyses based on GGDisnet-predicted genes, offering valuable clinical insights.
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Affiliation(s)
- Ying Wang
- Department of Endoscopy, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yaqi Du
- Department of Gastroenterology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
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6
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Momanyi BM, Zulfiqar H, Grace-Mercure BK, Ahmed Z, Ding H, Gao H, Liu F. CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations. Comput Biol Med 2023; 163:107165. [PMID: 37315383 DOI: 10.1016/j.compbiomed.2023.107165] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/31/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023]
Abstract
MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.
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Affiliation(s)
- Biffon Manyura Momanyi
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, 313001, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zahoor Ahmed
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, 313001, China
| | - Hui Ding
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Hui Gao
- School of Computer Science and Engineering, Center for Informational Biology, 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.
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Zhu W, Yuan SS, Li J, Huang CB, Lin H, Liao B. A First Computational Frame for Recognizing Heparin-Binding Protein. Diagnostics (Basel) 2023; 13:2465. [PMID: 37510209 PMCID: PMC10377868 DOI: 10.3390/diagnostics13142465] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.
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Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Shi-Shi Yuan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu 610106, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, ABa Teachers University, Chengdu 623002, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
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Su W, Qian X, Yang K, Ding H, Huang C, Zhang Z. Recognition of outer membrane proteins using multiple feature fusion. Front Genet 2023; 14:1211020. [PMID: 37351347 PMCID: PMC10284346 DOI: 10.3389/fgene.2023.1211020] [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: 04/24/2023] [Accepted: 05/24/2023] [Indexed: 06/24/2023] Open
Abstract
Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification. Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction. Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins.
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Affiliation(s)
- Wenxia Su
- College of Science, Inner Mongolia Agriculture University, Hohhot, China
| | - Xiaojun Qian
- School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Keli Yang
- Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji, China
| | - Hui Ding
- School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chengbing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
| | - Zhaoyue Zhang
- School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
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Kraus L, Beavens B. The Current Therapeutic Role of Chromatin Remodeling for the Prognosis and Treatment of Heart Failure. Biomedicines 2023; 11:biomedicines11020579. [PMID: 36831115 PMCID: PMC9953583 DOI: 10.3390/biomedicines11020579] [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: 01/24/2023] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
Cardiovascular diseases are a major cause of death globally, with no cure to date. Many interventions have been studied and suggested, of which epigenetics and chromatin remodeling have been the most promising. Over the last decade, major advancements have been made in the field of chromatin remodeling, particularly for the treatment of heart failure, because of innovations in bioinformatics and gene therapy. Specifically, understanding changes to the chromatin architecture have been shown to alter cardiac disease progression via variations in genomic sequencing, targeting cardiac genes, using RNA molecules, and utilizing chromatin remodeler complexes. By understanding these chromatin remodeling mechanisms in an injured heart, treatments for heart failure have been suggested through individualized pharmaceutical interventions as well as biomarkers for major disease states. By understanding the current roles of chromatin remodeling in heart failure, a potential therapeutic approach may be discovered in the future.
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Identification of adaptor proteins using the ANOVA feature selection technique. Methods 2022; 208:42-47. [DOI: 10.1016/j.ymeth.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
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11
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Li L, Qiu W, Lin L, Liu J, Shi X, Shi Y. Predicting recurrence and metastasis risk of endometrial carcinoma via prognostic signatures identified from multi-omics data. Front Oncol 2022; 12:982452. [PMID: 36059678 PMCID: PMC9438970 DOI: 10.3389/fonc.2022.982452] [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: 06/30/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesEndometrial carcinoma (EC) is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of EC are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of EC. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients.MethodsThe mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), copy number variation (CNV) data and clinical information of patients with EC were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers.ResultsWe achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation.ConclusionWe developed a computational model using omics information, which is able to predicting recurrence and metastasis risk of EC accurately.
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Affiliation(s)
- Ling Li
- Department of Gynecological Oncology Surgery, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China
| | - Wenjing Qiu
- Science System Department, Geneis Beijing Co., Ltd., Beijing, China
| | - Liang Lin
- Department of Gynecological Oncology Surgery, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China
| | - Jinyang Liu
- Science System Department, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiaoli Shi
- Science System Department, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
- *Correspondence: Yi Shi, ; Xiaoli Shi,
| | - Yi Shi
- Department of Molecular Pathology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China
- *Correspondence: Yi Shi, ; Xiaoli Shi,
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Rodriguez-Merchan EC. The current role of artificial intelligence in hemophilia. Expert Rev Hematol 2022; 15:927-931. [PMID: 35980129 DOI: 10.1080/17474086.2022.2114895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION The utilization of artificial intelligence (AI) in hemophilia is still in its early phases. AREAS COVERED In this paper a review of the available information on AI in hemophilia has been performed, to better understand the relationship between hemophilia and AI. Regarding the physical elements of AI (robotics), robotic-assisted total knee arthroplasty and laparoscopic prostatectomy have been successfully performed in hemophilic patients. Concerning the virtual elements of AI, machine learning (ML) in hemophilia has been used with encouraging results for the following: prediction of disease severity, recognition of factor V as an essential modifier of thrombin generation in mild to moderate hemophilia A, development hemophilia-focused user-centered app, gene therapy, estimation of the risk of myocardial infarction, and identification of CRISPR/Cas9 nuclease off-target for the treatment of hemophilia. AI is an emerging reality that can produce a paradigm shift in hemophilia. EXPERT OPINION Various AI systems can facilitate clinical care for professionals, improving the diagnosis and treatment of hemophilia. However, AI systems still have many limitations and raise operational and ethical issues. AI systems should be integrated prudently and reasonably within the practitioner's workflow.
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Affiliation(s)
- E Carlos Rodriguez-Merchan
- Department of Orthopedic Surgery, La Paz University Hospital, Madrid, Spain.,Osteoarticular Surgery Research, Hospital La Paz Institute for Health Research - IdiPAZ (La Paz University Hospital - Autonomous University of Madrid), Madrid, Spain
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13
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Yang J, Shi X, Wang B, Qiu W, Tian G, Wang X, Wang P, Yang J. Ultrasound Image Classification of Thyroid Nodules Based on Deep Learning. Front Oncol 2022; 12:905955. [PMID: 35912199 PMCID: PMC9335944 DOI: 10.3389/fonc.2022.905955] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/22/2022] [Indexed: 11/25/2022] Open
Abstract
A thyroid nodule, which is defined as abnormal growth of thyroid cells, indicates excessive iodine intake, thyroid degeneration, inflammation, and other diseases. Although thyroid nodules are always non-malignant, the malignancy likelihood of a thyroid nodule grows steadily every year. In order to reduce the burden on doctors and avoid unnecessary fine needle aspiration (FNA) and surgical resection, various studies have been done to diagnose thyroid nodules through deep-learning-based image recognition analysis. In this study, to predict the benign and malignant thyroid nodules accurately, a novel deep learning framework is proposed. Five hundred eight ultrasound images were collected from the Third Hospital of Hebei Medical University in China for model training and validation. First, a ResNet18 model, pretrained on ImageNet, was trained by an ultrasound image dataset, and a random sampling of training dataset was applied 10 times to avoid accidental errors. The results show that our model has a good performance, the average area under curve (AUC) of 10 times is 0.997, the average accuracy is 0.984, the average recall is 0.978, the average precision is 0.939, and the average F1 score is 0.957. Second, Gradient-weighted Class Activation Mapping (Grad-CAM) was proposed to highlight sensitive regions in an ultrasound image during the learning process. Grad-CAM is able to extract the sensitive regions and analyze their shape features. Based on the results, there are obvious differences between benign and malignant thyroid nodules; therefore, shape features of the sensitive regions are helpful in diagnosis to a great extent. Overall, the proposed model demonstrated the feasibility of employing deep learning and ultrasound images to estimate benign and malignant thyroid nodules.
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Affiliation(s)
- Jingya Yang
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan, China
- Scientific System, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoli Shi
- Scientific System, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Genesis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Bing Wang
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan, China
| | - Wenjing Qiu
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan, China
- Scientific System, Geneis Beijing Co., Ltd., Beijing, China
| | - Geng Tian
- Scientific System, Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Genesis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xudong Wang
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan, China
| | - Peizhen Wang
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan, China
- *Correspondence: Peizhen Wang, ; Jiasheng Yang,
| | - Jiasheng Yang
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan, China
- *Correspondence: Peizhen Wang, ; Jiasheng Yang,
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