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Qiu X, Wang H, Tan X, Fang Z. G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction. Comput Biol Med 2024; 173:108376. [PMID: 38552281 DOI: 10.1016/j.compbiomed.2024.108376] [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: 12/21/2023] [Revised: 03/21/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024]
Abstract
Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.
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Affiliation(s)
- Xihe Qiu
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Haoyu Wang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Xiaoyu Tan
- INF Technology (Shanghai) Co., Ltd., Shanghai, China
| | - Zhijun Fang
- School of Computer Science and Technology, Donghua University, Shanghai, China.
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Zhang Y, Deng Z, Xu X, Feng Y, Junliang S. Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review. J Chem Inf Model 2024; 64:2158-2173. [PMID: 37458400 DOI: 10.1021/acs.jcim.3c00582] [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] [Indexed: 04/09/2024]
Abstract
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.
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Affiliation(s)
- Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Zengqian Deng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Xiaoyu Xu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Yinfei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Shang Junliang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276800, China
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Suleman MT, Alturise F, Alkhalifah T, Khan YD. m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models. BioData Min 2024; 17:4. [PMID: 38360720 PMCID: PMC10868122 DOI: 10.1186/s13040-023-00353-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/31/2023] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites. OBJECTIVE Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated. METHODOLOGY The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models. RESULTS The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics. CONCLUSION For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .
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Affiliation(s)
- Muhammad Taseer Suleman
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia.
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan
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Wang NN, Zhu B, Li XL, Liu S, Shi JY, Cao DS. Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities. J Chem Inf Model 2024; 64:96-109. [PMID: 38132638 DOI: 10.1021/acs.jcim.3c01304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.
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Affiliation(s)
- Ning-Ning Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Bei Zhu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Xin-Liang Li
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Dong-Sheng Cao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P.R. China
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Imanpour A, Kolahi Azar H, Makarem D, Nematollahi Z, Nahavandi R, Rostami M, Beheshtizadeh N. In silico engineering and simulation of RNA interferences nanoplatforms for osteoporosis treating and bone healing promoting. Sci Rep 2023; 13:18185. [PMID: 37875547 PMCID: PMC10598124 DOI: 10.1038/s41598-023-45183-3] [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: 02/14/2023] [Accepted: 10/17/2023] [Indexed: 10/26/2023] Open
Abstract
Osteoporosis is a bone condition characterized by reduced bone mineral density (BMD), poor bone microarchitecture/mineralization, and/or diminished bone strength. This asymptomatic disorder typically goes untreated until it presents as a low-trauma fracture of the hip, spine, proximal humerus, pelvis, and/or wrist, requiring surgery. Utilizing RNA interference (RNAi) may be accomplished in a number of ways, one of which is by the use of very tiny RNA molecules called microRNAs (miRNAs) and small interfering RNAs (siRNAs). Several kinds of antagomirs and siRNAs are now being developed to prevent the detrimental effects of miRNAs. The goal of this study is to find new antagonists for miRNAs and siRNAs that target multiple genes in order to reduce osteoporosis and promote bone repair. Also, choosing the optimum nanocarriers to deliver these RNAis appropriately to the body could lighten up the research road. In this context, we employed gene ontology analysis to search across multiple datasets. Following data analysis, a systems biology approach was used to process it. A molecular dynamics (MD) simulation was used to explore the possibility of incorporating the suggested siRNAs and miRNA antagonists into polymeric bioresponsive nanocarriers for delivery purposes. Among the three nanocarriers tested [polyethylene glycol (PEG), polyethylenimine (PEI), and PEG-PEI copolymer], MD simulations show that the integration of PEG-PEI with has-mIR-146a-5p is the most stable (total energy = -372.84 kJ/mol, Gyration radius = 2.1084 nm), whereas PEI is an appropriate delivery carrier for has-mIR-7155. The findings of the systems biology and MD simulations indicate that the proposed RNAis might be given through bioresponsive nanocarriers to accelerate bone repair and osteoporosis treatment.
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Affiliation(s)
- Aylar Imanpour
- Regenerative Medicine Group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Hanieh Kolahi Azar
- Regenerative Medicine Group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Department of Pathology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Dorna Makarem
- Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Politecnica de Madrid, Madrid, Spain
| | - Zeinab Nematollahi
- UCL Department of Nanotechnology, Division of Surgery and Interventional Science, University College London, London, UK
| | - Reza Nahavandi
- Regenerative Medicine Group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Department of Biochemical and Pharmaceutical Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran
| | - Mohammadreza Rostami
- Food Science and Nutrition Group (FSAN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Division of Food Safety and Hygiene, Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Nima Beheshtizadeh
- Regenerative Medicine Group (REMED), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
- Department of Tissue Engineering, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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Han W, Wang N, Han M, Liu X, Sun T, Xu J. Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis. Cancer Med 2023; 12:19301-19319. [PMID: 37676050 PMCID: PMC10557844 DOI: 10.1002/cam4.6503] [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: 11/20/2022] [Revised: 07/22/2023] [Accepted: 08/25/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier. METHODS In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions. RESULTS The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances. CONCLUSIONS GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.
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Affiliation(s)
- Wenjie Han
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
| | - Na Wang
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
| | - Mengzhen Han
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
| | - Xiaolin Liu
- Liaoning Kanghui Biotechnology Co., LtdShenyangChina
| | - Tao Sun
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Key Laboratory of Liaoning Breast Cancer ResearchShenyangChina
- Department of Breast MedicineCancer Hospital of Dalian University of Technology, Liaoning Cancer HospitalShenyangChina
| | - Junnan Xu
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of Breast MedicineCancer Hospital of Dalian University of Technology, Liaoning Cancer HospitalShenyangChina
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Gellert GA, Rasławska-Socha J, Marcjasz N, Price T, Heyduk A, Mlodawska A, Kuszczyński K, Jędruch A, Orzechowski P. The Role of Virtual Triage in Improving Clinician Experience and Satisfaction: A Narrative Review. TELEMEDICINE REPORTS 2023; 4:180-191. [PMID: 37529770 PMCID: PMC10389257 DOI: 10.1089/tmr.2023.0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 08/03/2023]
Abstract
Objective This review examines the literature on improving clinician satisfaction with a focus on what has been most effective in improving experience from the perspective of clinicians, and the potential role that virtual triage (VT) technology can play in delivering positive clinician experiences that improve clinical care, and bring value to health care delivery organizations (HDOs). Methods Review and synthesis of evidence on clinician satisfaction indicating a potential for VT to favorably impact clinician experience, sense of effectiveness, efficiency, and reduction of administrative task burden. Analysis considers how to conceptualize and the value of improving clinician experience, leading clinician dissatisfiers, and the potential role of VT in improving clinician experience/satisfaction. Results Contributors to poor clinician experience/satisfaction where VT could have a beneficial impact include better managing resource limitations, administrative workload, lack of care coordination, information overload, and payer interactions. VT can improve clinician experience through the technology's ability to leverage real-time actionable data clinicians can use, streamlining patient-clinician communications, personalizing care delivery, optimizing care coordination, and better aligning digital/virtual services with clinical practice. From an organizational perspective, improvements in clinician experience and satisfaction derive from establishing an effective digital back door, increasing the clinical impact of and satisfaction derived from telemedicine and virtual care, and enhancing clinician centricity. Conclusions By embracing digital transformation and implementing solutions such as VT that focus on improving patient and clinician experience, HDOs can address barriers to delivery of high-quality, efficient, and cost-effective care. VT is a digital health tool that can create a more streamlined and satisfying experience for clinicians and the patients they care for. VT is a technology solution that can help clinicians make faster more informed decisions, reduces avoidable care, improves communication with patients and within care teams, and lowers their administrative burden so they have more quality time to care for patients.
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Affiliation(s)
- George A. Gellert
- Evidence-Based Impact and Value Demonstration, Infermedica Inc., San Antonio, Texas, USA
| | - Joanna Rasławska-Socha
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Natalia Marcjasz
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Tim Price
- Product Development, Infermedica Inc., London, United Kingdom
| | - Alicja Heyduk
- Implementation and Customer Success, Infermedica Inc., Wrocław, Poland
| | - Agata Mlodawska
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Kacper Kuszczyński
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Aleksandra Jędruch
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Piotr Orzechowski
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
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Kırboğa KK, Abbasi S, Küçüksille EU. Explainability and white box in drug discovery. Chem Biol Drug Des 2023; 102:217-233. [PMID: 37105727 DOI: 10.1111/cbdd.14262] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023]
Abstract
Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.
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Affiliation(s)
- Kevser Kübra Kırboğa
- Bioengineering Department, Bilecik Seyh Edebali University, Bilecik, Turkey
- Informatics Institute, Istanbul Technical University, Maslak, Turkey
| | - Sumra Abbasi
- Department of Biological Sciences, National of Medical Sciences, Rawalpindi, Pakistan
| | - Ecir Uğur Küçüksille
- Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey
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Hosseinzadeh Kasani P, Lee JE, Park C, Yun CH, Jang JW, Lee SA. Evaluation of nutritional status and clinical depression classification using an explainable machine learning method. Front Nutr 2023; 10:1165854. [PMID: 37229464 PMCID: PMC10203418 DOI: 10.3389/fnut.2023.1165854] [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: 02/14/2023] [Accepted: 03/27/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction Depression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored. Methods This study aimed to determine the ability of machine learning-based decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with cross-validation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpretable using model-agnostic explanations and Shapley additive explanation for the prediction at both the population and individual levels. Results The best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. The explainable results revealed a complementary observation of the relative changes in feature values, and, thus, the importance of emergent depression risks could be identified. Discussion The strength of our approach is the large sample size used for training with a fine-tuned model. The machine learning-based analysis showed that the hyper-tuned model has empirically higher accuracy in classifying patients with depressive disorder, as evidenced by the set of interpretable experiments, and can be an effective solution for disease control.
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Affiliation(s)
- Payam Hosseinzadeh Kasani
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Jung Eun Lee
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Chihyun Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Republic of Korea
| | - Cheol-Heui Yun
- Department of Agricultural Biotechnology, Seoul National University, Seoul, Republic of Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea
- Department of Neurology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea
| | - Sang-Ah Lee
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
- Department of Preventive Medicine, College of Medicine, Kangwon National University, Chuncheon, Republic of Korea
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Zhu J, Ge M, Chang Z, Dong W. CRCNet: Global-local context and multi-modality cross attention for polyp segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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11
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Saito S, Sakamoto S, Higuchi K, Sato K, Zhao X, Wakai K, Kanesaka M, Kamada S, Takeuchi N, Sazuka T, Imamura Y, Anzai N, Ichikawa T, Kawakami E. Machine-learning predicts time-series prognosis factors in metastatic prostate cancer patients treated with androgen deprivation therapy. Sci Rep 2023; 13:6325. [PMID: 37072487 PMCID: PMC10113215 DOI: 10.1038/s41598-023-32987-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/05/2023] [Indexed: 05/03/2023] Open
Abstract
Machine learning technology is expected to support diagnosis and prognosis prediction in medicine. We used machine learning to construct a new prognostic prediction model for prostate cancer patients based on longitudinal data obtained from age at diagnosis, peripheral blood and urine tests of 340 prostate cancer patients. Random survival forest (RSF) and survival tree were used for machine learning. In the time-series prognostic prediction model for metastatic prostate cancer patients, the RSF model showed better prediction accuracy than the conventional Cox proportional hazards model for almost all time periods of progression-free survival (PFS), overall survival (OS) and cancer-specific survival (CSS). Based on the RSF model, we created a clinically applicable prognostic prediction model using survival trees for OS and CSS by combining the values of lactate dehydrogenase (LDH) before starting treatment and alkaline phosphatase (ALP) at 120 days after treatment. Machine learning provides useful information for predicting the prognosis of metastatic prostate cancer prior to treatment intervention by considering the nonlinear and combined impacts of multiple features. The addition of data after the start of treatment would allow for more precise prognostic risk assessment of patients and would be beneficial for subsequent treatment selection.
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Affiliation(s)
- Shinpei Saito
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Chiba, Japan
| | - Shinichi Sakamoto
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan.
| | | | - Kodai Sato
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Chiba, Japan
| | - Xue Zhao
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
| | - Ken Wakai
- Teikyo University Chiba Medical Center, Ichihara, Chiba, Japan
| | - Manato Kanesaka
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
| | - Shuhei Kamada
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
| | - Nobuyoshi Takeuchi
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
| | - Tomokazu Sazuka
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
| | - Yusuke Imamura
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
| | - Naohiko Anzai
- Department of Pharmacology, Graduate School of Medicine, Chiba University, Chiba, Chiba, Japan
| | - Tomohiko Ichikawa
- Department of Urology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba, Chiba, 260-8670, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Chiba, Japan
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Kanagawa, Japan
- Institute for Advanced Academic Research (IAAR), Chiba University, Chiba, Chiba, Japan
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12
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Yang C. Prediction of hearing preservation after acoustic neuroma surgery based on SMOTE-XGBoost. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10757-10772. [PMID: 37322959 DOI: 10.3934/mbe.2023477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Prior to the surgical removal of an acoustic neuroma, the majority of patients anticipate that their hearing will be preserved to the greatest possible extent following surgery. This paper proposes a postoperative hearing preservation prediction model for the characteristics of class-imbalanced hospital real data based on the extreme gradient boost tree (XGBoost). In order to eliminate sample imbalance, the synthetic minority oversampling technique (SMOTE) is applied to increase the number of underclass samples in the data. Multiple machine learning models are also used for the accurate prediction of surgical hearing preservation in acoustic neuroma patients. In comparison to research results from existing literature, the experimental results found the model proposed in this paper to be superior. In summary, the method this paper proposes can make a significant contribution to the development of personalized preoperative diagnosis and treatment plans for patients, leading to effective judgment for the hearing retention of patients with acoustic neuroma following surgery, a simplified long medical treatment process and saved medical resources.
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Affiliation(s)
- Cenyi Yang
- School of Mathematics and Statistics, Central South University, Changsha 410083, China
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13
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Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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14
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Kang K, Wu Y, Han C, Wang L, Wang Z, Zhao A. Homologous recombination deficiency in triple-negative breast cancer: Multi-scale transcriptomics reveals distinct tumor microenvironments and limitations in predicting immunotherapy response. Comput Biol Med 2023; 158:106836. [PMID: 37031511 DOI: 10.1016/j.compbiomed.2023.106836] [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: 11/02/2022] [Revised: 02/17/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and has the highest proportion of homologous recombination deficiency (HRD). HRD has been considered a biomarker of response to immune checkpoint inhibitors (ICIs), but the reality is more complicated. A comprehensive comparison of the tumor microenvironment (TME) in HRD and non-HRD TNBC samples may be helpful. METHODS Datasets from single-cell, spatial, and bulk RNA-sequencing were collected to explore the role of HRD in the development of TME at multiple scales. Based on the findings in the TME, machine learning algorithms were used to construct a response prediction model in eleven ICI therapy cohorts. RESULTS A more exhausted phenotype of T cells and a more tolerogenic phenotype of dendritic cells were found in the non-HRD group. HRD reprograms the predominant phenotype of cancer-associated fibroblasts (CAFs) from myofibroblastic CAFs to inflammatory-like CAFs. As interactions between myofibroblastic CAFs and other cells, DPP4-chemokines associated with reduced immune cell recruitment were unique in the non-HRD group. The prediction model based on DPP4-related genes had acceptable performance in predicting response, prognosis, and immune cell content. Higher HRD scores in bulk RNA-sequencing samples indicated more activated immune cell function, but not higher immune cell content, which may be affected by factors such as antigen-presenting capacity. CONCLUSIONS Based on multi-scale transcriptomics, our findings comprehensively reveal differences in the TME between HRD and non-HRD samples. Combining HRD with the prediction model or other methods for assessing immune cell content, may better predict response to ICIs in TNBC.
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Affiliation(s)
- Kai Kang
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China; Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yijun Wu
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China; Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Chang Han
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Li Wang
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhile Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ailin Zhao
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China.
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15
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Babaei M, Evers TMJ, Shokri F, Altucci L, de Lange ECM, Mashaghi A. Biochemical reaction network topology defines dose-dependent Drug-Drug interactions. Comput Biol Med 2023; 155:106584. [PMID: 36805215 DOI: 10.1016/j.compbiomed.2023.106584] [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: 09/20/2022] [Revised: 12/28/2022] [Accepted: 01/22/2023] [Indexed: 01/29/2023]
Abstract
Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis-Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.
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Affiliation(s)
- Mehrad Babaei
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands; Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy.
| | - Tom M J Evers
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
| | - Fereshteh Shokri
- Leiden University Medical Center, Leiden, 2333ZA, the Netherlands.
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy; BIOGEM, Molecular Biology and Genetics Research Institute, Ariano Irpino, Italy.
| | - Elizabeth C M de Lange
- Predictive Pharmacology, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
| | - Alireza Mashaghi
- Medical Systems Biophysics and Bioengineering, Systems Pharmacology and Pharmacy Division, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2333CC, the Netherlands.
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16
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Alrowais F, Alotaibi SS, Hilal AM, Marzouk R, Mohsen H, Osman AE, Alneil AA, Eldesouki MI. Clinical Decision Support Systems to Predict Drug-Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2696. [PMID: 36768060 PMCID: PMC9916256 DOI: 10.3390/ijerph20032696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug-drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.
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Affiliation(s)
- Fadwa Alrowais
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Saud S. Alotaibi
- Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Makkah 24211, Saudi Arabia
| | - Anwer Mustafa Hilal
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
| | - Radwa Marzouk
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Heba Mohsen
- Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt
| | - Azza Elneil Osman
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
| | - Amani A. Alneil
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
| | - Mohamed I. Eldesouki
- Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 16436, Saudi Arabia
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17
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Explainable artificial intelligence as a reliable annotator of archaeal promoter regions. Sci Rep 2023; 13:1763. [PMID: 36720898 PMCID: PMC9889792 DOI: 10.1038/s41598-023-28571-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/20/2023] [Indexed: 02/02/2023] Open
Abstract
Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulation of their gene expression is essential. A key momentum in regulating genes responsible for the life maintenance of archaea is when transcription factor proteins bind to the promoter element. This DNA segment is conserved, which enables its exploration by machine learning techniques. Here, we trained and tested a support vector machine with 3935 known archaeal promoter sequences. All promoter sequences were coded into DNA Duplex Stability. After, we performed a model interpretation task to map the decision pattern of the classification procedure. We also used a dataset of known-promoter sequences for validation. Our results showed that an AT rich region around position - 27 upstream (relative to the start TSS) is the most conserved in the analyzed organisms. In addition, we were able to identify the BRE element (- 33), the PPE (at - 10) and a position at + 3, that provides a more understandable picture of how promoters are organized in all the archaeal organisms. Finally, we used the interpreted model to identify potential promoter sequences of 135 unannotated organisms, delivering regulatory regions annotation of archaea in a scale never accomplished before ( https://pcyt.unam.mx/gene-regulation/ ). We consider that this approach will be useful to understand how gene regulation is achieved in other organisms apart from the already established transcription factor binding sites.
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18
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Jiang L, Chen S, Wu Y, Zhou D, Duan L. Prediction of coronary heart disease in gout patients using machine learning models. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4574-4591. [PMID: 36896513 DOI: 10.3934/mbe.2023212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Growing evidence shows that there is an increased risk of cardiovascular diseases among gout patients, especially coronary heart disease (CHD). Screening for CHD in gout patients based on simple clinical factors is still challenging. Here we aim to build a diagnostic model based on machine learning so as to avoid missed diagnoses or over exaggerated examinations as much as possible. Over 300 patient samples collected from Jiangxi Provincial People's Hospital were divided into two groups (gout and gout+CHD). The prediction of CHD in gout patients has thus been modeled as a binary classification problem. A total of eight clinical indicators were selected as features for machine learning classifiers. A combined sampling technique was used to overcome the imbalanced problem in the training dataset. Eight machine learning models were used including logistic regression, decision tree, ensemble learning models (random forest, XGBoost, LightGBM, GBDT), support vector machine (SVM) and neural networks. Our results showed that stepwise logistic regression and SVM achieved more excellent AUC values, while the random forest and XGBoost models achieved more excellent performances in terms of recall and accuracy. Furthermore, several high-risk factors were found to be effective indices in predicting CHD in gout patients, which provide insights into the clinical diagnosis.
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Affiliation(s)
- Lili Jiang
- Department of Rheumatology and Clinical Immunology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Sirong Chen
- School of Mathematical Sciences, Soochow University, Suzhou, China
| | - Yuanhui Wu
- Department of Rheumatology and Clinical Immunology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China
| | - Lihua Duan
- Department of Rheumatology and Clinical Immunology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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19
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Prasad B, Bjourson AJ, Shukla P. muSignAl: An algorithm to search for multiple omic signatures with similar predictive performance. Proteomics 2023; 23:e2200252. [PMID: 36076312 DOI: 10.1002/pmic.202200252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/29/2022] [Accepted: 09/05/2022] [Indexed: 01/19/2023]
Abstract
Multidimensional omic datasets often have correlated features leading to the possibility of discovering multiple biological signatures with similar predictive performance for a phenotype. However, their exploration is limited by low sample size and the exponential nature of the combinatorial search leading to high computational cost. To address these issues, we have developed an algorithm muSignAl (multiple signature algorithm) which selects multiple signatures with similar predictive performance while systematically bypassing the requirement of exploring all the combinations of features. We demonstrated the workflow of this algorithm with an example of proteomics dataset. muSignAl is applicable in various bioinformatics-driven explorations, such as understanding the relationship between multiple biological feature sets and phenotypes, and discovery and development of biomarker panels while providing the opportunity of optimising their development cost with the help of equally good multiple signatures. Source code of muSignAl is freely available at https://github.com/ShuklaLab/muSignAl.
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Affiliation(s)
- Bodhayan Prasad
- Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Londonderry, BT47 6SB, UK
| | - Anthony J Bjourson
- Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Londonderry, BT47 6SB, UK
| | - Priyank Shukla
- Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Londonderry, BT47 6SB, UK
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20
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Li J, Cao F, Gao Q, Liang K, Tang Y. Improving diagnosis accuracy of non-small cell lung carcinoma on noisy data by adaptive group lasso regularized multinomial regression. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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Suleman MT, Alturise F, Alkhalifah T, Khan YD. iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models. Digit Health 2023; 9:20552076231165963. [PMID: 37009307 PMCID: PMC10064468 DOI: 10.1177/20552076231165963] [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: 12/29/2022] [Accepted: 03/09/2023] [Indexed: 04/04/2023] Open
Abstract
Background Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification. Objective The modification also triggers lung cancer in humans. The identification of D sites was carried out through conventional laboratory methods; however, those were costly and time-consuming. The readiness of RNA sequences helps in the identification of D sites through computationally intelligent models. However, the most challenging part is turning these biological sequences into distinct vectors. Methods The current research proposed novel feature extraction mechanisms and the identification of D sites in tRNA sequences using ensemble models. The ensemble models were then subjected to evaluation using k-fold cross-validation and independent testing. Results The results revealed that the stacking ensemble model outperformed all the ensemble models by revealing 0.98 accuracy, 0.98 specificity, 0.97 sensitivity, and 0.92 Matthews Correlation Coefficient. The proposed model, iDHU-Ensem, was also compared with pre-existing predictors using an independent test. The accuracy scores have shown that the proposed model in this research study performed better than the available predictors. Conclusion The current research contributed towards the enhancement of D site identification capabilities through computationally intelligent methods. A web-based server, iDHU-Ensem, was also made available for the researchers at https://taseersuleman-idhu-ensem-idhu-ensem.streamlit.app/.
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Affiliation(s)
- Muhammad Taseer Suleman
- Department of Computer Science, School of systems and technology, University of Management and Technology, Lahore, Pakistan
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia
- Fahad Alturise, Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia.
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School of systems and technology, University of Management and Technology, Lahore, Pakistan
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22
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Pagano TP, dos Santos LL, Santos VR, Sá PHM, Bonfim YDS, Paranhos JVD, Ortega LL, Nascimento LFS, Santos A, Rönnau MM, Winkler I, Nascimento EGS. Remote Heart Rate Prediction in Virtual Reality Head-Mounted Displays Using Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2022; 22:9486. [PMID: 36502188 PMCID: PMC9738680 DOI: 10.3390/s22239486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/22/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Head-mounted displays are virtual reality devices that may be equipped with sensors and cameras to measure a patient's heart rate through facial regions. Heart rate is an essential body signal that can be used to remotely monitor users in a variety of situations. There is currently no study that predicts heart rate using only highlighted facial regions; thus, an adaptation is required for beats per minute predictions. Likewise, there are no datasets containing only the eye and lower face regions, necessitating the development of a simulation mechanism. This work aims to remotely estimate heart rate from facial regions that can be captured by the cameras of a head-mounted display using state-of-the-art EVM-CNN and Meta-rPPG techniques. We developed a region of interest extractor to simulate a dataset from a head-mounted display device using stabilizer and video magnification techniques. Then, we combined support vector machine and FaceMash to determine the regions of interest and adapted photoplethysmography and beats per minute signal predictions to work with the other techniques. We observed an improvement of 188.88% for the EVM and 55.93% for the Meta-rPPG. In addition, both models were able to predict heart rate using only facial regions as input. Moreover, the adapted technique Meta-rPPG outperformed the original work, whereas the EVM adaptation produced comparable results for the photoplethysmography signal.
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Affiliation(s)
- Tiago Palma Pagano
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Lucas Lisboa dos Santos
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Victor Rocha Santos
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Paulo H. Miranda Sá
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Yasmin da Silva Bonfim
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | | | - Lucas Lemos Ortega
- Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | | | - Alexandre Santos
- HP Inc. Brazil R&D, Porto Alegre 90619-900, Rio Grande do Sul, Brazil
| | | | - Ingrid Winkler
- Department of Management and Industrial Technology, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
| | - Erick G. Sperandio Nascimento
- Department of Management and Industrial Technology, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil
- Faculty of Engineering and Physical Sciences, School of Computer Science and Electronic Engineering, Surrey Institute for People-Centred AI, University of Surrey, Guildford GU2 7XH, UK
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23
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Yuan L, Ji M, Wang S, Wen X, Huang P, Shen L, Xu J. Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study. BMC Med Inform Decis Mak 2022; 22:312. [PMID: 36447180 PMCID: PMC9707001 DOI: 10.1186/s12911-022-02066-3] [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: 06/28/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. OBJECTIVE To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. METHODS 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. RESULTS Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08-8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67-7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28-5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14-9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. CONCLUSION Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.
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Affiliation(s)
- Lei Yuan
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.412632.00000 0004 1758 2270Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
| | - Mengyao Ji
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Shuo Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Xinyu Wen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Pingxiao Huang
- grid.33199.310000 0004 0368 7223Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei China
| | - Lei Shen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Jun Xu
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
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24
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Hoa Vo T, Thi Kim Nguyen N, Quoc Khanh Le N. Improved prediction of drug-drug interactions using ensemble deep neural networks. MEDICINE IN DRUG DISCOVERY 2022. [DOI: 10.1016/j.medidd.2022.100149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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25
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Zhao G, Wang H, Li Z. Capillary water absorption values estimation of building stones by ensembled and hybrid SVR models. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA). Although meticulous experimental studies are needed to determine a rock’s CWA, predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR) models were created: a traditional SVR, two ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR). Results show that all models have acceptable performance in predicting the CWA with R 2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods.
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Affiliation(s)
- Guiping Zhao
- School of Civil Engineering, Shandong Polytechnic, Jinan, China
| | - Hongmei Wang
- School of Business, Shandong Jianzhu University, Jinan 250101, China
| | - Zhanfa Li
- Shandong Provincial Academy of Architectural Science Co., Ltd. Jinan, China
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Yan Y, Bao X, Chen B, Li Y, Yin J, Zhu G, Li Q. Interpretable machine learning framework reveals microbiome features of oral disease. Microbiol Res 2022; 265:127198. [PMID: 36126491 DOI: 10.1016/j.micres.2022.127198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/25/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although the oral microbiome plays an important role in the progression of oral diseases, the microbes closely related to these diseases remain largely uncharacterized. RESULTS We collected saliva samples from 140 individuals and performed 16 S amplicon sequencing. An interpretable machine learning framework for imbalanced high-dimensional big data of clinical microbial samples was developed to identify 14 oral microbiome features associated with oral diseases. Microbiome risk scores (MRSs) with the identified features were constructed with SHapley Additive exPlanations (SHAP). Correlations of the MRSs with individual physiological indicators and lifestyle habits were calculated. CONCLUSION Our results reveal a set of oral microbiome features associated with oral diseases. Our study demonstrates the feasibility of preventing oral disease through lifestyle interventions and provides a reference method for the era of precision medicine aimed at individualized medicine.
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Affiliation(s)
- Yueyang Yan
- Key Laboratory for Zoonoses Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun 130062, China
| | - Xin Bao
- Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130021, China
| | - Bohua Chen
- Department of Stomatology, The Fifth Affiliated Hospital of Sun Yat-sen University, 52 Meihua East Road, Xiangzhou District, Zhuhai City, Guangdong Province, China
| | - Ying Li
- Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Jigang Yin
- Key Laboratory for Zoonoses Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun 130062, China
| | - Guan Zhu
- Key Laboratory for Zoonoses Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun 130062, China
| | - Qiushi Li
- Key Laboratory for Zoonoses Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun 130062, China; Department of Stomatology, The Fifth Affiliated Hospital of Sun Yat-sen University, 52 Meihua East Road, Xiangzhou District, Zhuhai City, Guangdong Province, China.
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Sidorczuk K, Gagat P, Pietluch F, Kała J, Rafacz D, Bąkała L, Słowik J, Kolenda R, Rödiger S, Fingerhut LCHW, Cooke IR, Mackiewicz P, Burdukiewicz M. Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data. Brief Bioinform 2022; 23:6672903. [PMID: 35988923 PMCID: PMC9487607 DOI: 10.1093/bib/bbac343] [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: 05/30/2022] [Revised: 07/07/2022] [Accepted: 07/25/2022] [Indexed: 12/29/2022] Open
Abstract
Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.
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Affiliation(s)
| | | | | | - Jakub Kała
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Dominik Rafacz
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Laura Bąkała
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Jadwiga Słowik
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Rafał Kolenda
- Quadram Institute Biosciences, Norwich Research Park, Norwich, United Kingdom,Wrocław University of Environmental and Life Sciences, Faculty of Veterinary Medicine, Poland
| | - Stefan Rödiger
- Brandenburg University of Technology Cottbus-Senftenberg, Faculty of Natural Sciences, Germany
| | - Legana C H W Fingerhut
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Australia
| | - Ira R Cooke
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Australia
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A miRNA Target Prediction Model Based on Distributed Representation Learning and Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4490154. [PMID: 35924115 PMCID: PMC9343202 DOI: 10.1155/2022/4490154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/16/2022] [Accepted: 06/07/2022] [Indexed: 11/18/2022]
Abstract
MicroRNAs (miRNAs) are a kind of noncoding RNA, which plays an essential role in gene regulation by binding to messenger RNAs (mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the nucleotide sequence. In this paper, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site-level prediction, which is inspired by the similarity between natural language and biological sequences. First, the word2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the embedding is extracted automatically via the stacked bidirectional long short-term memory (BiLSTM) network. By testing, our method can effectively improve the accuracy, sensitivity, specificity, and F-measure of other methods. Through our research, it is proved that the distributed representation can improve the accuracy of the deep learning model and better solve the miRNA target site prediction problem.
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29
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Validation of a New Telenursing Questionnaire: Testing the Test. MATHEMATICS 2022. [DOI: 10.3390/math10142463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background: Existing surveys on telenursing refer to specific areas of nursing after the implementation of a programme, but telenursing in general has not been fully evaluated from a prospective approach. Aim: Design and statistical validation of a telenursing questionnaire. Methods: A new questionnaire was designed with 18 paired (to avoid leading) questions (Likert-5) plus three dichotomous questions (randomly ordered, inspired by existing validated tests) to analyse the dimensions of: acceptance, usefulness and appropriateness of telenursing from the nursing point of view (7 min test). The questionnaire was validated by classical tests and item response tests (Rasch) using six computer-generated databases with different response profiles (tendency to be positioned against, neutral and positioned in favour) with two degrees of agreement between each pair of responses for each option. Results: Classical testing: Cronbach’s alphas (from 0.8 to 0.95), Kaiser–Meyer–Olkin (KMO) (0.93 to 0.95) and a significant p < 0.0001 for Bartlett’s test of sphericity were obtained. Rasch analysis: Reliability coefficients (0.94). Warm’s mean weighted likelihood estimates (0.94). Extreme infit-t and outfit-t values (+1.61 to −1.98). Conclusions: Both the classical test and the Rasch approaches confirm the usefulness of the new test for assessing nurses’ positioning in relation to telenursing.
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Khorramifar A, Rasekh M, Karami H, Covington JA, Derakhshani SM, Ramos J, Gancarz M. Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27113508. [PMID: 35684450 PMCID: PMC9182414 DOI: 10.3390/molecules27113508] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 12/19/2022]
Abstract
Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.
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Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
| | - Mansour Rasekh
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
- Correspondence: (M.R.); (H.K.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +98-912-083-9910 (H.K.)
| | - Hamed Karami
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
- Correspondence: (M.R.); (H.K.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +98-912-083-9910 (H.K.)
| | | | - Sayed M. Derakhshani
- Wageningen Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA Wageningen, The Netherlands;
| | - Jose Ramos
- College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA;
| | - Marek Gancarz
- Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
- Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Krakow, Poland
- Correspondence: (M.R.); (H.K.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +98-912-083-9910 (H.K.)
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Bahado-Singh RO, Radhakrishna U, Gordevičius J, Aydas B, Yilmaz A, Jafar F, Imam K, Maddens M, Challapalli K, Metpally RP, Berrettini WH, Crist RC, Graham SF, Vishweswaraiah S. Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease. Cells 2022; 11:cells11111744. [PMID: 35681440 PMCID: PMC9179874 DOI: 10.3390/cells11111744] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 02/01/2023] Open
Abstract
Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer’s disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.
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Affiliation(s)
- Ray O. Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Correspondence: (U.R.); (S.V.); Tel.: +1-248-551-2574 (U.R.); +1-248-551-2569 (S.V.)
| | - Juozas Gordevičius
- Vugene, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA;
| | - Buket Aydas
- Department of Care Management Analytics, Blue Cross Blue Shield of Michigan, Detroit, MI 48226, USA;
| | - Ali Yilmaz
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Alzheimer’s Disease Research, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Faryal Jafar
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Khaled Imam
- Department of Internal Medicine, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (K.I.); (M.M.)
| | - Michael Maddens
- Department of Internal Medicine, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (K.I.); (M.M.)
| | - Kshetra Challapalli
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Raghu P. Metpally
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA 17821, USA; (R.P.M.); (W.H.B.)
| | - Wade H. Berrettini
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA 17821, USA; (R.P.M.); (W.H.B.)
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Richard C. Crist
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Department of Alzheimer’s Disease Research, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Correspondence: (U.R.); (S.V.); Tel.: +1-248-551-2574 (U.R.); +1-248-551-2569 (S.V.)
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Guan R, Pang H, Liang Y, Shao Z, Gao X, Xu D, Feng X. Discovering trends and hotspots of biosafety and biosecurity research via machine learning. Brief Bioinform 2022; 23:6590367. [PMID: 35596953 PMCID: PMC9487701 DOI: 10.1093/bib/bbac194] [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: 02/03/2022] [Revised: 04/06/2022] [Accepted: 04/27/2022] [Indexed: 11/14/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as ‘biosafety’) a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.
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Affiliation(s)
- Renchu Guan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China.,Zhuhai Sub Laboratory, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Zhuhai College of Science and Technology, Zhuhai, 519041, Guangdong, China
| | - Haoyu Pang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China
| | - Yanchun Liang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China.,Zhuhai Sub Laboratory, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Zhuhai College of Science and Technology, Zhuhai, 519041, Guangdong, China
| | - Zhongjun Shao
- Department of Epidemiology, Ministry of Education Key Laboratory of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Xin Gao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.,Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.,BioMap, Beijing, 100192, China
| | - Dong Xu
- Department of Electric Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, 65201, Missouri, USA
| | - Xiaoyue Feng
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China
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Halder AK, Moura AS, Cordeiro MNDS. Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next? Int J Mol Sci 2022; 23:ijms23094937. [PMID: 35563327 PMCID: PMC9099502 DOI: 10.3390/ijms23094937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/24/2022] [Accepted: 04/28/2022] [Indexed: 01/27/2023] Open
Abstract
Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, West Bengal, India
| | - Ana S. Moura
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
| | - Maria Natália D. S. Cordeiro
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
- Correspondence: ; Tel.: +35-12-2040-2502
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Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study. Diagnostics (Basel) 2022; 12:diagnostics12040991. [PMID: 35454039 PMCID: PMC9027004 DOI: 10.3390/diagnostics12040991] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/08/2022] [Accepted: 04/13/2022] [Indexed: 12/22/2022] Open
Abstract
Background: The presence of heavy calcification in the coronary artery always presents a challenge for coronary computed tomography angiography (CCTA) in assessing the degree of coronary stenosis due to blooming artifacts associated with calcified plaques. Our study purpose was to use an advanced artificial intelligence (enhanced super-resolution generative adversarial network [ESRGAN]) model to suppress the blooming artifact in CCTA and determine its effect on improving the diagnostic performance of CCTA in calcified plaques. Methods: A total of 184 calcified plaques from 50 patients who underwent both CCTA and invasive coronary angiography (ICA) were analysed with measurements of coronary lumen on the original CCTA, and three sets of ESRGAN-processed images including ESRGAN-high-resolution (ESRGAN-HR), ESRGAN-average and ESRGAN-median with ICA as the reference method for determining sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: ESRGAN-processed images improved the specificity and PPV at all three coronary arteries (LAD-left anterior descending, LCx-left circumflex and RCA-right coronary artery) compared to original CCTA with ESRGAN-median resulting in the highest values being 41.0% (95% confidence interval [CI]: 30%, 52.7%) and 26.9% (95% CI: 22.9%, 31.4%) at LAD; 41.7% (95% CI: 22.1%, 63.4%) and 36.4% (95% CI: 28.9%, 44.5%) at LCx; 55% (95% CI: 38.5%, 70.7%) and 47.1% (95% CI: 38.7%, 55.6%) at RCA; while corresponding values for original CCTA were 21.8% (95% CI: 13.2%, 32.6%) and 22.8% (95% CI: 20.8%, 24.9%); 12.5% (95% CI: 2.6%, 32.4%) and 27.6% (95% CI: 24.7%, 30.7%); 17.5% (95% CI: 7.3%, 32.8%) and 32.7% (95% CI: 29.6%, 35.9%) at LAD, LCx and RCA, respectively. There was no significant effect on sensitivity and NPV between the original CCTA and ESRGAN-processed images at all three coronary arteries. The area under the receiver operating characteristic curve was the highest with ESRGAN-median images at the RCA level with values being 0.76 (95% CI: 0.64, 0.89), 0.81 (95% CI: 0.69, 0.93), 0.82 (95% CI: 0.71, 0.94) and 0.86 (95% CI: 0.76, 0.96) corresponding to original CCTA and ESRGAN-HR, average and median images, respectively. Conclusions: This feasibility study shows the potential value of ESRGAN-processed images in improving the diagnostic value of CCTA for patients with calcified plaques.
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Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput Struct Biotechnol J 2022; 20:2112-2123. [PMID: 35832629 PMCID: PMC9092071 DOI: 10.1016/j.csbj.2022.04.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/15/2022] [Accepted: 04/15/2022] [Indexed: 12/26/2022] Open
Abstract
A systematic review on applications of explainable AI in drug-drug interaction prediction. Review is conducted on a comprehensive set of 94 papers from five prestigious databases. Discussions on the promises and challenges of explainable AI algorithms for drug-drug interaction prediction.
Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.
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Affiliation(s)
- Thanh Hoa Vo
- Master Program in Clinical Genomics and Proteomics, College of Pharmacy, Taipei Medical University, Taipei 110, Taiwan
| | - Ngan Thi Kim Nguyen
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan
| | - Quang Hien Kha
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Corresponding author at: Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan.
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Prediction of Injuries in CrossFit Training: A Machine Learning Perspective. ALGORITHMS 2022. [DOI: 10.3390/a15030077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been raised over the potential injury risks that are associated with its training including rhabdomyolysis and musculoskeletal injuries. However, identification of risk factors for predicting injuries in CrossFit athletes has been limited by the absence of relevant big epidemiological studies. The main purpose of this paper is the identification of risk factors and the development of machine learning-based models using ensemble learning that can predict CrossFit injuries. To accomplish the aforementioned targets, a survey-based epidemiological study was conducted in Greece to collect data on musculoskeletal injuries in CrossFit practitioners. A Machine Learning (ML) pipeline was then implemented that involved data pre-processing, feature selection and well-known ML models. The performance of the proposed ML models was assessed using a comprehensive cross validation mechanism whereas a discussion on the nature of the selected features is also provided. An area under the curve (AUC) of 77.93% was achieved by the best ML model using ensemble learning (Adaboost) on the group of six selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to numerous performance metrics including accuracy, sensitivity, specificity, AUC and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of injuries in CrossFit.
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