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Zhang Y, Yao L, Chung CR, Huang Y, Li S, Zhang W, Pang Y, Lee TY. KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data. iScience 2024; 27:109333. [PMID: 38523792 PMCID: PMC10959666 DOI: 10.1016/j.isci.2024.109333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 12/07/2023] [Accepted: 02/21/2024] [Indexed: 03/26/2024] Open
Abstract
Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.
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Affiliation(s)
- Yuntian Zhang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Lantian Yao
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320953, Taiwan
| | - Yixian Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wenyang Zhang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuxuan Pang
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
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2
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Wang R, Chung CR, Lee TY. Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species. Int J Mol Sci 2024; 25:2869. [PMID: 38474116 DOI: 10.3390/ijms25052869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/19/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications across diverse species. Therefore, a versatile computational approach is necessary for interpretable analysis of RNA modifications across species. A multi-scale biological language-based deep learning model is proposed for interpretable, sequential-level prediction of diverse RNA modifications. Benchmark comparisons across species demonstrate the model's superiority in predicting various RNA methylation types over current state-of-the-art methods. The cross-species validation and attention weight visualization also highlight the model's capability to capture sequential and functional semantics from genomic backgrounds. Our analysis of RNA modifications helps us find the potential existence of "biological grammars" in each modification type, which could be effective for mapping methylation-related sequential patterns and understanding the underlying biological mechanisms of RNA modifications.
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Affiliation(s)
- Rulan Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
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3
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Yao L, Guan J, Li W, Chung CR, Deng J, Chiang YC, Lee TY. Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation. Anal Chem 2024; 96:1538-1546. [PMID: 38226973 DOI: 10.1021/acs.analchem.3c04196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).
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Affiliation(s)
- Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Wenshuo Li
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, 320317 Taoyuan, Taiwan
| | - Junyang Deng
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
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Guan J, Yao L, Chung CR, Xie P, Zhang Y, Deng J, Chiang YC, Lee TY. Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning. J Chem Inf Model 2023; 63:7886-7898. [PMID: 38054927 DOI: 10.1021/acs.jcim.3c01602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they can have side effects and resistance issues. In this backdrop, anti-inflammatory peptides (AIPs) have emerged as a promising therapeutic approach against inflammation. Leveraging machine learning methods, we have the opportunity to accelerate the discovery and investigation of these AIPs more effectively. In this study, we proposed an advanced framework by ensemble machine learning and deep learning for AIP prediction. Initially, we constructed three individual models with extremely randomized trees (ET), gated recurrent unit (GRU), and convolutional neural networks (CNNs) with attention mechanism and then used stacking architecture to build the final predictor. By utilizing various sequence encodings and combining the strengths of different algorithms, our predictor demonstrated exemplary performance. On our independent test set, our model achieved an accuracy, MCC, and F1-score of 0.757, 0.500, and 0.707, respectively, clearly outperforming other contemporary AIP prediction methods. Additionally, our model offers profound insights into the feature interpretation of AIPs, establishing a valuable knowledge foundation for the design and development of future anti-inflammatory strategies.
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Affiliation(s)
- Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Peilin Xie
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yilun Zhang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Junyang Deng
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
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Chung CR, Liou JT, Wu LC, Horng JT, Lee TY. Multi-label classification and features investigation of antimicrobial peptides with various functional classes. iScience 2023; 26:108250. [PMID: 38025779 PMCID: PMC10679894 DOI: 10.1016/j.isci.2023.108250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/15/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
The challenge of drug-resistant bacteria to global public health has led to increased attention on antimicrobial peptides (AMPs) as a targeted therapeutic alternative with a lower risk of resistance. However, high production costs and limitations in functional class prediction have hindered progress in this field. In this study, we used multi-label classifiers with binary relevance and algorithm adaptation techniques to predict different functions of AMPs across a wide range of pathogen categories, including bacteria, mammalian cells, fungi, viruses, and cancer cells. Our classifiers attained promising AUC scores varying from 0.8492 to 0.9126 on independent testing data. Forward feature selection identified sequence order and charge as critical, with specific amino acids (C and E) as discriminative. These findings provide valuable insights for the design of antimicrobial peptides (AMPs) with multiple functionalities, thus contributing to the broader effort to combat drug-resistant pathogens.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Jhen-Ting Liou
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taoyuan City, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
- Center for Intelligent Drug Systems and Smart Biodevices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
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6
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Lin Y, Lin WY, Lin TW, Tseng YJ, Wang YC, Yu JR, Chung CR, Wang HY. Trend of HPV Molecular Epidemiology in the Post-Vaccine Era: A 10-Year Study. Viruses 2023; 15:2015. [PMID: 37896791 PMCID: PMC10612033 DOI: 10.3390/v15102015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 10/29/2023] Open
Abstract
Cervical cancer, a major health concern among women worldwide, is closely linked to human papillomavirus (HPV) infection. This study explores the evolving landscape of HPV molecular epidemiology in Taiwan over a decade (2010-2020), where prophylactic HPV vaccination has been implemented since 2007. Analyzing data from 40,561 vaginal swab samples, with 42.0% testing positive for HPV, we reveal shifting trends in HPV genotype distribution and infection patterns. The 12 high-risk genotypes, in order of decreasing percentage, were HPV 52, 58, 16, 18, 51, 56, 39, 59, 33, 31, 45, and 35. The predominant genotypes were HPV 52, 58, and 16, accounting for over 70% of cases annually. The proportions of high-risk and non-high-risk HPV infections varied across age groups. High-risk infections predominated in sexually active individuals aged 30-50 and were mixed-type infections. The composition of high-risk HPV genotypes was generally stable over time; however, HPV31, 33, 39, and 51 significantly decreased over the decade. Of the strains, HPV31 and 33 are shielded by the nonavalent HPV vaccine. However, no reduction was noted for the other seven genotypes. This study offers valuable insights into the post-vaccine HPV epidemiology. Future investigations should delve into HPV vaccines' effects and their implications for cervical cancer prevention strategies. These findings underscore the need for continued surveillance and research to guide effective public health interventions targeting HPV-associated diseases.
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Affiliation(s)
- Yueh Lin
- Department of Family Medicine, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan;
| | - Wan-Ying Lin
- Department of Medicine, University of California San Diego, San Diego, CA 92093, USA;
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA;
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Ting-Wei Lin
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan; (T.-W.L.); (J.-R.Y.)
| | - Yi-Ju Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Yu-Chiang Wang
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA;
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Jia-Ruei Yu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan; (T.-W.L.); (J.-R.Y.)
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan;
| | - Hsin-Yao Wang
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA;
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan; (T.-W.L.); (J.-R.Y.)
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7
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Wang Z, Pang Y, Chung CR, Wang HY, Cui H, Chiang YC, Horng JT, Lu JJ, Lee TY. A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology. Brief Bioinform 2023; 24:bbad330. [PMID: 37742050 DOI: 10.1093/bib/bbad330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/19/2023] [Accepted: 08/31/2023] [Indexed: 09/25/2023] Open
Abstract
The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus.
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Affiliation(s)
- Zhuo Wang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong 518172, China
| | - Yuxuan Pang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Haiyan Cui
- Department of Clinical Laboratory, Longgang District People's Hospital of Shenzhen & The Second Affiliated Hospital of the Chinese University of Hong Kong, Shenzhen, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan 33303, Taiwan
- Department of Medicine, College of Medicine, Chang Gung University, Taoyuan 33303, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
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Guan J, Yao L, Chung CR, Chiang YC, Lee TY. StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture. Int J Mol Sci 2023; 24:10348. [PMID: 37373494 DOI: 10.3390/ijms241210348] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues. However, identifying THPs experimentally, using methods such as phage display or in vivo screening, is a complex, time-consuming task, hence the need for computational methods. In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture. With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods. It achieved an accuracy of 0.915 and a 0.831 Matthews Correlation Coefficient (MCC) score on the main dataset, and an accuracy of 0.883 and a 0.767 MCC score on the small dataset. StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs. Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.
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Affiliation(s)
- Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Road, Shenzhen 518172, China
| | - Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Road, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
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Chung CR, Wang HY, Yao CH, Wu LC, Lu JJ, Horng JT, Lee TY. Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data. Microbiol Spectr 2023; 11:e0347922. [PMID: 37042778 PMCID: PMC10269626 DOI: 10.1128/spectrum.03479-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 02/21/2023] [Indexed: 04/13/2023] Open
Abstract
In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. IMPORTANCE Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for the two-stage model training, and the values of the area under the receiver operating characteristic curve (AUC) were 0.62 for AMC, 0.72 for CAZ, 0.87 for CIP, 0.72 for CRO, and 0.72 for CXM. Further investigations revealed that the informative peak m/z 9714 appeared with some important peaks at m/z 6809, m/z 7650, m/z 10534, and m/z 11783 for CIP and at m/z 6809, m/z 10475, and m/z 8447 for CAZ, CRO, and CXM. This framework has the potential to improve the accuracy by approximately 2.8%, indicating a promising potential for further research.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Chun-Han Yao
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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10
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Wang R, Chung CR, Huang HD, Lee TY. Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences. Brief Bioinform 2023; 24:7008797. [PMID: 36715277 DOI: 10.1093/bib/bbac573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 01/31/2023] Open
Abstract
N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms are time-consuming and labor-intensive, and most of the proposed methods focused on limited species types. To further understand the relevant biological mechanisms among different species with the same RNA modification, it is necessary to develop a computational scheme that can be applied to different species. To achieve this, we proposed an attention-based deep learning method, adaptive-m6A, which consists of convolutional neural network, bi-directional long short-term memory and an attention mechanism, to identify m6A sites in multiple species. In addition, three conventional machine learning (ML) methods, including support vector machine, random forest and logistic regression classifiers, were considered in this work. In addition to the performance of ML methods for multi-species prediction, the optimal performance of adaptive-m6A yielded an accuracy of 0.9832 and the area under the receiver operating characteristic curve of 0.98. Moreover, the motif analysis and cross-validation among different species were conducted to test the robustness of one model towards multiple species, which helped improve our understanding about the sequence characteristics and biological functions of RNA modifications in different species.
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Affiliation(s)
- Rulan Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
| | - Chia-Ru Chung
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Life Sciences, University of Science and Technology of China, 230026, Hefei, Anhui, P.R. China
| | - Hsien-Da Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
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11
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Yao L, Li W, Zhang Y, Deng J, Pang Y, Huang Y, Chung CR, Yu J, Chiang YC, Lee TY. Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation. Int J Mol Sci 2023; 24:ijms24054328. [PMID: 36901759 PMCID: PMC10001941 DOI: 10.3390/ijms24054328] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.
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Affiliation(s)
- Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Wenshuo Li
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Yuntian Zhang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Junyang Deng
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Yuxuan Pang
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Yixian Huang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Jinhan Yu
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Correspondence: (Y.-C.C.); (T.-Y.L.)
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Correspondence: (Y.-C.C.); (T.-Y.L.)
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12
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Chung CR, Wang HY, Chou PH, Wu LC, Lu JJ, Horng JT, Lee TY. Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra. Int J Mol Sci 2023; 24:ijms24020998. [PMID: 36674514 PMCID: PMC9865071 DOI: 10.3390/ijms24020998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.
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Affiliation(s)
- Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan 333323, Taiwan
| | - Po-Han Chou
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan 333323, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan 333323, Taiwan
| | - Jorng-Tzong Horng
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- Correspondence: (J.-T.H.); (T.-Y.L.)
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Correspondence: (J.-T.H.); (T.-Y.L.)
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13
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Chen CC, Wu EHK, Chen YQ, Tsai HJ, Chung CR, Yeh SC. Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder. IEEE Trans Neural Syst Rehabil Eng 2023; PP. [PMID: 37022368 DOI: 10.1109/tnsre.2023.3241649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Early diagnosis and treatment can reduce the symptoms of Attention Deficit/Hyperactivity Disorder (ADHD) in children, but medical diagnosis is usually delayed. Hence, it is important to increase the efficiency of early diagnosis. Previous studies used behavioral and neuronal data during GO/NOGO task to help detect ADHD and the accuracy differed considerably from 53% to 92%, depending on the employed methods and the number of electroencephalogram (EEG) channels. It remains unclear whether data from a few EEG channels can still lead to a good accuracy of detecting ADHD. Here, we hypothesize that introducing distractions into a VR-based GO/NOGO task can augment the detection of ADHD using 6-channel EEG because children with ADHD are easily distracted. Forty-nine ADHD children and 32 typically developing children were recruited. We use a clinically applicable system with EEG to record data. Statistical analysis and machine learning methods were employed to analyze the data. The behavioral results revealed significant differences in task performance when there are distractions. The presence of distractions leads to EEG changes in both groups, indicating immaturity in inhibitory control. Importantly, the distractions additionally enhanced the between-group differences in NOGO α and γ power, reflecting insufficient inhibition in different neural networks for distraction suppression in the ADHD group. Machine learning methods further confirmed that distractions enhance the detection of ADHD with an accuracy of 85.45%. In conclusion, this system can assist in fast screenings for ADHD and the findings of neuronal correlates of distractions can help design therapeutic strategies.
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Affiliation(s)
- Chun-Chuan Chen
- Department of Biomedical Sciences and Engineering, National Central University and Institute of Cognitive Neuroscience, Taiwan
| | - Eric Hsiao-Kuang Wu
- Computer Science and Information Engineering Department, National Central University, Taiwan
| | - Yan-Qing Chen
- Computer Science and Information Engineering Department, National Central University, Taiwan
| | - Ho-Jung Tsai
- Computer Science and Information Engineering Department, National Central University, Taiwan
| | - Chia-Ru Chung
- Computer Science and Information Engineering Department, National Central University, Taiwan
| | - Shih-Ching Yeh
- Computer Science and Information Engineering Department, National Central University, Taiwan
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14
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Wang HY, Kuo CH, Chung CR, Lin WY, Wang YC, Lin TW, Yu JR, Lu JJ, Wu TS. Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms. Biomedicines 2022; 11:biomedicines11010045. [PMID: 36672552 PMCID: PMC9856018 DOI: 10.3390/biomedicines11010045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072-0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI-TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment.
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Affiliation(s)
- Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Chi-Heng Kuo
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | | | - Yu-Chiang Wang
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Ting-Wei Lin
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Jia-Ruei Yu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
- School of Medicine, Chang Gung University, Taoyuan City 333323, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan City 333323, Taiwan
| | - Ting-Shu Wu
- Division of Infectious Diseases, Departments of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan City 333423, Taiwan
- Correspondence: ; Tel.: +886-3-3281200-7955
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15
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Chung CR, Su MC, Lee SH, Wu EHK, Tang LH, Yeh SC. An Intelligent Motor Assessment Method Utilizing a Bi-Lateral Virtual-Reality Task for Stroke Rehabilitation on Upper Extremity. IEEE J Transl Eng Health Med 2022; 10:2100811. [PMID: 36457894 PMCID: PMC9704741 DOI: 10.1109/jtehm.2022.3213348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 04/29/2022] [Accepted: 06/30/2022] [Indexed: 11/06/2022]
Abstract
Virtual reality (VR) has been widely adopted by therapists to provide rich motor training tasks. Time series data of motion trajectory accompanied with the interaction of VR system may contain important clues in regard to the assessment of motor function, however, clinical evaluation scales such as Fugl-Meyer Assessment (FMA), Wolf Motor Function Test (WMFT), and Test D'évaluation Des Membres Supérieurs Des Personnes Âgées (TEMPA) are highly depended in clinic. Further, there is not yet an assessment method that simultaneously consider motion trajectory and clinical evaluation scales. The objective of this study is to establish an evidence-based assessment model by machine-learning method that integrated motion trajectory of a VR task with clinical evaluation scales. In this study, a VR system for upper-limb motor training was proposed for stroke rehabilitation. Clinical trials with 20 stroke patients were performed. A variety of motor indicators that derived via motion trajectory were proposed. The correlations between motor indicators and clinical evaluation scales were examined. Further, motor indicators were integrated with evaluation scales to develop a machine-learning based model that represents an evidence-based motor assessment approach. Clinical evaluation scales, FMA, TEMPA and WMFT, were significantly progressed. A few motor indicators were found significantly correlated with clinical evaluation scales. The accuracy of machine-learning based assessment model was up to 86%. The proposed VR system is validated to be effective in motor rehabilitation. Motor indicators derived from motor trajectory were with potential for clinical motor assessment. Machine learning could be a promising tool to perform automatic assessment. Clinical and Translational Impact Statement-A VR task for motor rehabilitation was exanimated via clinical trials. Integrating motor indices with clinical assessment, a machine-learning model with accuracy of 86% was developed to evaluate motor function.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information EngineeringNational Central University Taoyuan 320 Taiwan
| | - Mu-Chun Su
- Department of Computer Science and Information EngineeringNational Central University Taoyuan 320 Taiwan
| | - Si-Huei Lee
- Department of Physical Medicine and RehabilitationTaipei Veterans General HospitalNational Yang-Ming University Taipei 11221 Taiwan
| | - Eric Hsiao-Kuang Wu
- Department of Computer Science and Information EngineeringNational Central University Taoyuan 320 Taiwan
| | - Li-Hsien Tang
- Department of Computer Science and Information EngineeringNational Central University Taoyuan 320 Taiwan
| | - Shih-Ching Yeh
- Department of Computer Science and Information EngineeringNational Central University Taoyuan 320 Taiwan
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16
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Chen CC, Chung CR, Tsai MC, Wu EHK, Chiu PR, Tsai PY, Yeh SC. Impaired Brain-Heart Relation in Patients With Methamphetamine Use Disorder During VR Induction of Drug Cue Reactivity. IEEE J Transl Eng Health Med 2022; 12:1-9. [PMID: 38059128 PMCID: PMC10697298 DOI: 10.1109/jtehm.2022.3206333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 07/12/2022] [Accepted: 09/08/2022] [Indexed: 12/08/2023]
Abstract
Methamphetamine use disorder (MUD) is an illness associated with severe health consequences. Virtual reality (VR) is used to induce the drug-cue reactivity and significant EEG and ECG abnormalities were found in MUD patients. However, whether a link exists between EEG and ECG abnormalities in patients with MUD during exposure to drug cues remains unknown. This is important from the therapeutic viewpoint because different treatment strategies may be applied when EEG abnormalities and ECG irregularities are complications of MUD. We designed a VR system with drug cues and EEG and ECG were recorded during VR exposure. Sixteen patients with MUD and sixteen healthy subjects were recruited. Statistical tests and Pearson correlation were employed to analyze the EEG and ECG. The results showed that, during VR induction, the patients with MUD but not healthy controls showed significant [Formula: see text] and [Formula: see text] power increases when the stimulus materials were most intense. This finding indicated that the stimuli are indiscriminate to healthy controls but meaningful to patients with MUD. Five heart rate variability (HRV) indexes significantly differed between patients and controls, suggesting abnormalities in the reaction of patient's autonomic nervous system. Importantly, significant relations between EEG and HRV indexes changes were only identified in the controls, but not in MUD patients, signifying a disruption of brain-heart relations in patients. Our findings of stimulus-specific EEG changes and the impaired brain-heart relations in patients with MUD shed light on the understanding of drug-cue reactivity and may be used to design diagnostic and/or therapeutic strategies for MUD.
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Affiliation(s)
- Chun-Chuan Chen
- Department of Biomedical Sciences and EngineeringNational Central UniversityTaoyuan City320317Taiwan
| | - Chia-Ru Chung
- Computer Science and Information Engineering DepartmentNational Central UniversityTaoyuan City320317Taiwan
| | - Meng-Chang Tsai
- Department of PsychiatryKaohsiung Chang Gung Memorial HospitalKaohsiung City83301Taiwan
- Department of PsychiatryChang Gung University College of MedicineKaohsiung City83301Taiwan
| | - Eric Hsiao-Kuang Wu
- Computer Science and Information Engineering DepartmentNational Central UniversityTaoyuan City320317Taiwan
| | - Po-Ru Chiu
- Department of Biomedical Sciences and EngineeringNational Central UniversityTaoyuan City320317Taiwan
| | - Po-Yi Tsai
- Department of Physical Medicine and RehabilitationTaipei Veterans General HospitalTaipei112201Taiwan
| | - Shih-Ching Yeh
- Computer Science and Information Engineering DepartmentNational Central UniversityTaoyuan City320317Taiwan
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17
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Wang HY, Hsieh TT, Chung CR, Chang HC, Horng JT, Lu JJ, Huang JH. Efficiently Predicting Vancomycin Resistance of Enterococcus Faecium From MALDI-TOF MS Spectra Using a Deep Learning-Based Approach. Front Microbiol 2022; 13:821233. [PMID: 35756017 PMCID: PMC9231590 DOI: 10.3389/fmicb.2022.821233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/28/2022] [Indexed: 11/13/2022] Open
Abstract
Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting Enterococcus faecium, which is one of the leading pathogens in the world. We developed a CNN model to rapidly and accurately predict vancomycin-resistant Enterococcus faecium (VREfm) samples from the whole mass spectra profiles of clinical samples. The CNN models demonstrated good classification performances with the average area under the receiver operating characteristic curve (AUROC) of 0.887 when using external validation data independently. Additionally, we employed the score-class activation mapping (CAM) method to identify the important features of our CNN models and found some discriminative signals that can substantially contribute to detecting the ion of resistance. This study not only utilized the complete information of MALTI-TOF MS data directly but also provided a practical means for rapid detection of VREfm using a deep learning algorithm.
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Affiliation(s)
- Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | | | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | | | - Jorng-Tzong Horng
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
- *Correspondence: Jorng-Tzong Horng
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan
- Jang-Jih Lu
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18
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Zhang J, Wang Z, Wang HY, Chung CR, Horng JT, Lu JJ, Lee TY. Rapid Antibiotic Resistance Serial Prediction in Staphylococcus aureus Based on Large-Scale MALDI-TOF Data by Applying XGBoost in Multi-Label Learning. Front Microbiol 2022; 13:853775. [PMID: 35495667 PMCID: PMC9039744 DOI: 10.3389/fmicb.2022.853775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/11/2022] [Indexed: 12/01/2022] Open
Abstract
Multidrug resistance has become a phenotype that commonly exists among Staphylococcus aureus and is a serious concern for infection treatment. Nowadays, to detect the antibiotic susceptibility, antibiotic testing is generated based on the level of genomic for cure decision consuming huge of time and labor, while matrix-assisted laser desorption-ionization (MALDI) time-of-flight mass spectrometry (TOF/MS) shows its possibility in high-speed and effective detection on the level of proteomic. In this study, on the basis of MALDI-TOF spectra data of discovery cohort with 26,852 samples and replication cohort with 4,963 samples from Taiwan area and their corresponding susceptibilities to oxacillin and clindamycin, a multi-label prediction model against double resistance using Lowest Power set ensemble with XGBoost is constructed for rapid susceptibility prediction. With the output of serial susceptibility prediction, the model performance can realize 77% of accuracy for the serial prediction, the area under the receiver characteristic curve of 0.93 for oxacillin susceptibility prediction, and the area under the receiver characteristic curve of 0.89 for clindamycin susceptibility prediction. The generated multi-label prediction model provides serial antibiotic resistance, such as the susceptibilities of oxacillin and clindamycin in this study, for S. aureus-infected patients based on MALDI-TOF, which will provide guidance in antibiotic usage during the treatment taking the advantage of speed and efficiency.
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Affiliation(s)
- Jiahong Zhang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan.,Department of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China
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19
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Wang C, Wang Z, Wang HY, Chung CR, Horng JT, Lu JJ, Lee TY. Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods. Front Microbiol 2022; 13:827451. [PMID: 35356528 PMCID: PMC8959214 DOI: 10.3389/fmicb.2022.827451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/17/2022] [Indexed: 11/30/2022] Open
Abstract
Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae, which leads to the increase in the risk of initial antibiotic selection for Klebsiella pneumoniae treatment. Rapid and precise identification of ciprofloxacin-resistant Klebsiella pneumoniae (CIRKP) is essential for clinical therapy. Nowadays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is another approach to discover antibiotic-resistant bacteria due to its shorter inspection time and lower cost than other current methods. Machine learning methods are introduced to assist in discovering significant biomarkers from MALDI-TOF MS data and construct prediction models for rapid antibiotic resistance identification. This study examined 16,997 samples taken from June 2013 to February 2018 as part of a longitudinal investigation done by Change Gung Memorial Hospitals (CGMH) at the Linkou branch. We applied traditional statistical approaches to identify significant biomarkers, and then a comparison was made between high-importance features in machine learning models and statistically selected features. Large-scale data guaranteed the statistical power of selected biomarkers. Besides, clustering analysis analyzed suspicious sub-strains to provide potential information about their influences on antibiotic resistance identification performance. For modeling, to simulate the real antibiotic resistance predicting challenges, we included basic information about patients and the types of specimen carriers into the model construction process and separated the training and testing sets by time. Final performance reached an area under the receiver operating characteristic curve (AUC) of 0.89 for support vector machine (SVM) and extreme gradient boosting (XGB) models. Also, logistic regression and random forest models both achieved AUC around 0.85. In conclusion, models provide sensitive forecasts of CIRKP, which may aid in early antibiotic selection against Klebsiella pneumoniae. The suspicious sub-strains could affect the model performance. Further works could keep on searching for methods to improve both the model accuracy and stability.
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Affiliation(s)
- Chunxuan Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Data Science, The Chinese University of Hong Kong, Shenzhen, China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan.,Department of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Data Science, The Chinese University of Hong Kong, Shenzhen, China
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20
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Chen CC, Tsai MC, Wu EHK, Chung CR, Lee Y, Chiu PR, Tsai PY, Sheng SR, Yeh SC. Neuronal Abnormalities Induced by an Intelligent Virtual Reality System for Methamphetamine Use Disorder. IEEE J Biomed Health Inform 2022; 26:3458-3465. [PMID: 35226611 DOI: 10.1109/jbhi.2022.3154759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Methamphetamine use disorder (MUD) is a brain disease that leads to altered regional neuronal activity. Virtual reality (VR) is used to induce the drug cue reactivity. Previous studies reported significant frequency-specific abnormalities in patients with MUD during VR induction of drug craving. However, whether those patients exhibit neuronal abnormalities after VR induction that could serve as the treatment target remains unclear. Here, we developed an integrated VR system for inducing drug related changes and investigated the neuronal abnormalities after VR exposure in patients. Fifteen patients with MUD and ten healthy subjects were recruited and exposed to drug-related VR environments. Resting-state EEG were recorded for 5 minutes twice-before and after VR and transformed to obtain the frequency-specific data. Three self-reported scales for measurement of the anxiety levels and impulsivity of participants were obtained after VR task. Statistical tests and machine learning methods were employed to reveal the differences between patients and healthy subjects. The result showed that patients with MUD and healthy subjects significantly differed in, and power changes after VR. These neuronal abnormalities in patients were associated with the self-reported behavioral scales, indicating impaired impulse control. Our findings of resting-state EEG abnormalities in patients with MUD after VR exposure have the translation value and can be used to develop the treatment strategies for methamphetamine use disorder.
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21
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Yu JR, Chen CH, Huang TW, Lu JJ, Chung CR, Lin TW, Wu MH, Tseng YJ, Wang HY. Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study. J Med Internet Res 2022; 24:e28036. [PMID: 35076405 PMCID: PMC8826151 DOI: 10.2196/28036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/31/2021] [Accepted: 10/04/2021] [Indexed: 12/27/2022] Open
Abstract
Background The use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied. Objective The aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms—logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms—when applied to clinical laboratory datasets. Methods We applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5. Results The experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency. Conclusions The XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.
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Affiliation(s)
- Jia-Ruei Yu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Chun-Hsien Chen
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
- Department of Information Management, Chang Gung University, Taoyuan City, Taiwan
| | - Tsung-Wei Huang
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan
| | - Ting-Wei Lin
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Min-Hsien Wu
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan
| | - Yi-Ju Tseng
- Department of Information Management, National Central University, Taoyuan City, Taiwan
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan
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22
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Chung CR, Wang Z, Weng JM, Wang HY, Wu LC, Tseng YJ, Chen CH, Lu JJ, Horng JT, Lee TY. MDRSA: A Web Based-Tool for Rapid Identification of Multidrug Resistant Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry. Front Microbiol 2021; 12:766206. [PMID: 34925273 PMCID: PMC8678511 DOI: 10.3389/fmicb.2021.766206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/28/2021] [Indexed: 11/19/2022] Open
Abstract
As antibiotics resistance on superbugs has risen, more and more studies have focused on developing rapid antibiotics susceptibility tests (AST). Meanwhile, identification of multiple antibiotics resistance on Staphylococcus aureus provides instant information which can assist clinicians in administrating the appropriate prescriptions. In recent years, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a powerful tool in clinical microbiology laboratories for the rapid identification of bacterial species. Yet, lack of study devoted on providing efficient methods to deal with the MS shifting problem, not to mention to providing tools incorporating the MALDI-TOF MS for the clinical use which deliver the instant administration of antibiotics to the clinicians. In this study, we developed a web tool, MDRSA, for the rapid identification of oxacillin-, clindamycin-, and erythromycin-resistant Staphylococcus aureus. Specifically, the kernel density estimation (KDE) was adopted to deal with the peak shifting problem, which is critical to analyze mass spectra data, and machine learning methods, including decision trees, random forests, and support vector machines, which were used to construct the classifiers to identify the antibiotic resistance. The areas under the receiver operating the characteristic curve attained 0.8 on the internal (10-fold cross validation) and external (independent testing) validation. The promising results can provide more confidence to apply these prediction models in the real world. Briefly, this study provides a web-based tool to provide rapid predictions for the resistance of antibiotics on Staphylococcus aureus based on the MALDI-TOF MS data. The web tool is available at: http://fdblab.csie.ncu.edu.tw/mdrsa/.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Zhuo Wang
- School of Life and Health Sciences, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China
| | - Jing-Mei Weng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Yi-Ju Tseng
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Chun-Hsien Chen
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,Department of Information Management, Chang Gung University, Taoyuan, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.,College of Medicine, Chang Gung University, Taoyuan, Taiwan.,Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
| | - Tzong-Yi Lee
- School of Life and Health Sciences, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China
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23
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Jhong JH, Yao L, Pang Y, Li Z, Chung CR, Wang R, Li S, Li W, Luo M, Ma R, Huang Y, Zhu X, Zhang J, Feng H, Cheng Q, Wang C, Xi K, Wu LC, Chang TH, Horng JT, Zhu L, Chiang YC, Wang Z, Lee TY. dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data. Nucleic Acids Res 2021; 50:D460-D470. [PMID: 34850155 PMCID: PMC8690246 DOI: 10.1093/nar/gkab1080] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/16/2021] [Accepted: 10/25/2021] [Indexed: 12/26/2022] Open
Abstract
The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.
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Affiliation(s)
- Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Zhongyan Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Rulan Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wenshuo Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Mengqi Luo
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Renfei Ma
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuqi Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xiaoning Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jiahong Zhang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hexiang Feng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Qifan Cheng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chunxuan Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Kun Xi
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 10675, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Lizhe Zhu
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Ying-Chih Chiang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
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24
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Tsai CF, Chen CC, Wu EHK, Chung CR, Huang CY, Tsai PY, Yeh SC. A Machine-Learning-Based Assessment Method for Early-Stage Neurocognitive Impairment by an Immersive Virtual Supermarket. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2124-2132. [PMID: 34623270 DOI: 10.1109/tnsre.2021.3118918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder. Though it is not yet curable or reversible, research has shown that clinical intervention or intensive cognitive training at an early stage may effectively delay the progress of the disease. As a result, screening populations with mild cognitive impairment (MCI) or early AD via efficient, effective and low-cost cognitive assessments is important. Currently, a cognitive assessment relies mostly on cognitive tests, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), which must be performed by therapists. Also, cognitive functions can be divided into a variety of dimensions, such as memory, attention, executive function, visual spatial and so on. Executive functions (EF), also known as executive control or cognitive control, refer to a set of skills necessary to perform higher-order cognitive processes, including working memory, planning, attention, cognitive flexibility, and inhibitory control. Along with the fast progress of virtual reality (VR) and artificial intelligence (AI), this study proposes an intelligent assessment method aimed at assessing executive functions. Utilizing machine learning to develop an automatic evidence-based assessment model, behavioral information is acquired through performing executive-function tasks in a VR supermarket. Clinical trials were performed individuals with MCI or early AD and six healthy participants. Statistical analysis showed that 45 out of 46 indices derived from behavioral information were found to differ significantly between individuals with neurocognitive disorder and healthy participants. This analysis indicates these indices may be potential bio-markers. Further, machine-learning methods were applied to build classifiers that differentiate between individuals with MCI or early AD and healthy participants. The accuracy of the classifier is up to 100%, demonstrating the derived features from the VR system were highly related to diagnosis of individuals with MCI or early AD.
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25
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Wang HY, Chung CR, Wang Z, Li S, Chu BY, Horng JT, Lu JJ, Lee TY. A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra. Brief Bioinform 2021; 22:bbaa138. [PMID: 32672791 PMCID: PMC8138823 DOI: 10.1093/bib/bbaa138] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/01/2020] [Accepted: 06/05/2020] [Indexed: 12/21/2022] Open
Abstract
Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410-2419, 2450-2459 and 6590-6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future.
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Affiliation(s)
- Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China
| | - Bo-Yu Chu
- Department of Computer Science & Engineering, Yuan Ze University, Taoyuan City, Taiwan
| | - Jorng-Tzong Horng
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Jang-Jih Lu
- Department of Computer Science and Information Engineering, National Central University, Taiwan
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, School of Life and Health Sciences
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26
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Tsai MC, Chung CR, Chen CC, Chen JY, Yeh SC, Lin CH, Chen YJ, Tsai MC, Wang YL, Lin CJ, Wu EHK. An Intelligent Virtual-Reality System With Multi-Model Sensing for Cue-Elicited Craving in Patients With Methamphetamine Use Disorder. IEEE Trans Biomed Eng 2021; 68:2270-2280. [PMID: 33571085 DOI: 10.1109/tbme.2021.3058805] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Methamphetamine abuse is getting worse amongst the younger population. While there is methadone or buprenorphine harm-reduction treatment for heroin addicts, there is no drug treatment for addicts with methamphetamine use disorder (MUD). Recently, non-medication treatment, such as the cue-elicited craving method integrated with biofeedback, has been widely used. Further, virtual reality (VR) is proposed to simulate an immersive virtual environment for cue-elicited craving in therapy. In this study, we developed a VR system equipped with flavor simulation for the purpose of inducing cravings for MUD patients in therapy. The VR system was integrated with multi-model sensors, such as an electrocardiogram (ECG), galvanic skin response (GSR) and eye tracking to measure various physiological responses from MUD patients in the virtual environment. The goal of the study was to validate the effectiveness of the proposed VR system in inducing the craving of MUD patients via the physiological data. Clinical trials were performed with 20 MUD patients and 11 healthy subjects. VR stimulation was applied to each subject and the physiological data was measured at the time of pre-VR stimulation and post-VR stimulation. A variety of features were extracted from the raw data of heart rate variability (HRV), GSR and eye tracking. The results of statistical analysis found that quite a few features of HRV, GSR and eye tracking had significant differences between pre-VR stimulation and post-VR stimulation in MUD patients but not in healthy subjects. Also, the data of post-VR stimulation showed a significant difference between MUD patients and healthy subjects. Correlation analysis was made and several features between HRV and GSR were found to be correlated. Further, several machine learning methods were applied and showed that the classification accuracy between MUD and healthy subjects at post-VR stimulation attained to 89.8%. In conclusion, the proposed VR system was validated to effectively induce the drug craving in MUD patients.
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27
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Wang Z, Wang HY, Chung CR, Horng JT, Lu JJ, Lee TY. Large-scale mass spectrometry data combined with demographics analysis rapidly predicts methicillin resistance in Staphylococcus aureus. Brief Bioinform 2020; 22:5983719. [PMID: 33197936 DOI: 10.1093/bib/bbaa293] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/04/2020] [Accepted: 10/04/2020] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND A mass spectrometry-based assessment of methicillin resistance in Staphylococcus aureus would have huge potential in addressing fast and effective prediction of antibiotic resistance. Since delays in the traditional antibiotic susceptibility testing, methicillin-resistant S. aureus remains a serious threat to human health. RESULTS Here, linking a 7 years of longitudinal study from two cohorts in the Taiwan area of over 20 000 individually resolved methicillin susceptibility testing results, we identify associations of methicillin resistance with the demographics and mass spectrometry data. When combined together, these connections allow for machine-learning-based predictions of methicillin resistance, with an area under the receiver operating characteristic curve of >0.85 in both the discovery [95% confidence interval (CI) 0.88-0.90] and replication (95% CI 0.84-0.86) populations. CONCLUSIONS Our predictive model facilitates early detection for methicillin resistance of patients with S. aureus infection. The large-scale antibiotic resistance study has unbiasedly highlighted putative candidates that could improve trials of treatment efficiency and inform on prescriptions.
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Affiliation(s)
- Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
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Yeh SC, Lin SY, Wu EHK, Zhang KF, Xiu X, Rizzo A, Chung CR. A Virtual-Reality System Integrated With Neuro-Behavior Sensing for Attention-Deficit/Hyperactivity Disorder Intelligent Assessment. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1899-1907. [PMID: 32746303 DOI: 10.1109/tnsre.2020.3004545] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Attention-deficit/Hyperactivity disorder(ADHD) is a common neurodevelopmental disorder among children. Traditional assessment methods generally rely on behavioral rating scales (BRS) performed by clinicians, and sometimes parents or teachers. However, BRS assessment is time consuming, and the subjective ratings may lead to bias for the evaluation. Therefore, the major purpose of this study was to develop a Virtual Reality (VR) classroom associated with an intelligent assessment model to assist clinicians for the diagnosis of ADHD. In this study, an immersive VR classroom embedded with sustained and selective attention tasks was developed in which visual, audio, and visual-audio hybrid distractions, were triggered while attention tasks were conducted. A clinical experiment with 37 ADHD and 31 healthy subjects was performed. Data from BRS was compared with VR task performance and analyzed by rank-sum tests and Pearson Correlation. Results showed that 23 features out of total 28 were related to distinguish the ADHD and non-ADHD children. Several features of task performance and neuro-behavioral measurements were also correlated with features of the BRSs. Additionally, the machine learning models incorporating task performance and neuro-behavior were used to classify ADHD and non-ADHD children. The mean accuracy for the repeated cross-validation reached to 83.2%, which demonstrated a great potential for our system to provide more help for clinicians on assessment of ADHD.
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Chung CR, Chang YP, Hsu YL, Chen S, Wu LC, Horng JT, Lee TY. Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins. Sci Rep 2020; 10:10541. [PMID: 32601280 PMCID: PMC7324624 DOI: 10.1038/s41598-020-67384-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 06/03/2020] [Indexed: 12/22/2022] Open
Abstract
Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level,
the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information,
and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Ya-Ping Chang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Yu-Lin Hsu
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Siyu Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan. .,Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41359, Taiwan.
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, 518172, China. .,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China.
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Wang HY, Chen CH, Shi S, Chung CR, Wen YH, Wu MH, Lebowitz MS, Zhou J, Lu JJ. Improving Multi-Tumor Biomarker Health Check-up Tests with Machine Learning Algorithms. Cancers (Basel) 2020; 12:E1442. [PMID: 32492934 PMCID: PMC7352838 DOI: 10.3390/cancers12061442] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 05/22/2020] [Accepted: 05/29/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Tumor markers are used to screen tens of millions of individuals worldwide at annual health check-ups, especially in East Asia. Machine learning (ML)-based algorithms that improve the diagnostic accuracy and clinical utility of these tests can have substantial impact leading to the early diagnosis of cancer. METHODS ML-based algorithms, including a cancer screening algorithm and a secondary organ of origin algorithm, were developed and validated using a large real world dataset (RWD) from asymptomatic individuals undergoing routine cancer screening at a Taiwanese medical center between May 2001 and April 2015. External validation was performed using data from the same period from a separate medical center. The data set included tumor marker values, age, and gender from 27,938 individuals, including 342 subsequently confirmed cancer cases. RESULTS Separate gender-specific cancer screening algorithms were developed. For men, a logistic regression-based algorithm outperformed single-marker and other ML-based algorithms, with a mean area under the receiver operating characteristic curve (AUROC) of 0.7654 in internal and 0.8736 in external cross validation. For women, a random forest-based algorithm attained a mean AUROC of 0.6665 in internal and 0.6938 in external cross validation. The median time to cancer diagnosis (TTD) in men was 451.5, 204.5, and 28 days for the mild, moderate, and high-risk groups, respectively; for women, the median TTD was 229, 132, and 125 days for the mild, moderate, and high-risk groups. A second algorithm was developed to predict the most likely affected organ systems for at-risk individuals. The algorithm yielded 0.8120 sensitivity and 0.6490 specificity for men, and 0.8170 sensitivity and 0.6750 specificity for women. CONCLUSIONS ML-derived algorithms, trained and validated by using a RWD, can significantly improve tumor marker-based screening for multiple types of early stage cancers, suggest the tissue of origin, and provide guidance for patient follow-up.
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Affiliation(s)
- Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 33305, Taiwan; (H.-Y.W.); (C.-H.C.); (Y.-H.W.)
- 20/20 GeneSystems, Inc., Rockville, MD 20850, USA; (S.S.); (M.S.L.)
- Program in Biomedical Engineering, Chang Gung University, Taoyuan City 33301, Taiwan
| | - Chun-Hsien Chen
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 33305, Taiwan; (H.-Y.W.); (C.-H.C.); (Y.-H.W.)
- Department of Information Management, Chang Gung University, Taoyuan City 33301, Taiwan
| | - Steve Shi
- 20/20 GeneSystems, Inc., Rockville, MD 20850, USA; (S.S.); (M.S.L.)
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan;
| | - Ying-Hao Wen
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 33305, Taiwan; (H.-Y.W.); (C.-H.C.); (Y.-H.W.)
| | - Min-Hsien Wu
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan City 33301, Taiwan;
| | | | - Jiming Zhou
- 20/20 GeneSystems, Inc., Rockville, MD 20850, USA; (S.S.); (M.S.L.)
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 33305, Taiwan; (H.-Y.W.); (C.-H.C.); (Y.-H.W.)
- 20/20 GeneSystems, Inc., Rockville, MD 20850, USA; (S.S.); (M.S.L.)
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan City 33301, Taiwan
- Department of Medicine, College of Medicine, Chang Gung University, Taoyuan City 33301, Taiwan
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Wang HY, Li WC, Huang KY, Chung CR, Horng JT, Hsu JF, Lu JJ, Lee TY. Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques. BMC Bioinformatics 2019; 20:703. [PMID: 31870283 PMCID: PMC6929280 DOI: 10.1186/s12859-019-3282-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 11/18/2019] [Indexed: 12/04/2022] Open
Abstract
Background Group B streptococcus (GBS) is an important pathogen that is responsible for invasive infections, including sepsis and meningitis. GBS serotyping is an essential means for the investigation of possible infection outbreaks and can identify possible sources of infection. Although it is possible to determine GBS serotypes by either immuno-serotyping or geno-serotyping, both traditional methods are time-consuming and labor-intensive. In recent years, the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported as an effective tool for the determination of GBS serotypes in a more rapid and accurate manner. Thus, this work aims to investigate GBS serotypes by incorporating machine learning techniques with MALDI-TOF MS to carry out the identification. Results In this study, a total of 787 GBS isolates, obtained from three research and teaching hospitals, were analyzed by MALDI-TOF MS, and the serotype of the GBS was determined by a geno-serotyping experiment. The peaks of mass-to-charge ratios were regarded as the attributes to characterize the various serotypes of GBS. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), were then used to construct predictive models for the five different serotypes (Types Ia, Ib, III, V, and VI). After optimization of feature selection and model generation based on training datasets, the accuracies of the selected models attained 54.9–87.1% for various serotypes based on independent testing data. Specifically, for the major serotypes, namely type III and type VI, the accuracies were 73.9 and 70.4%, respectively. Conclusion The proposed models have been adopted to implement a web-based tool (GBSTyper), which is now freely accessible at http://csb.cse.yzu.edu.tw/GBSTyper/, for providing efficient and effective detection of GBS serotypes based on a MALDI-TOF MS spectrum. Overall, this work has demonstrated that the combination of MALDI-TOF MS and machine intelligence could provide a practical means of clinical pathogen testing.
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Affiliation(s)
- Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, 33305, Taiwan.,Program in Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan
| | - Wen-Chi Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Kai-Yao Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan.,Department of Bioinformatics and Medical Engineering, Asia University, Taoyuan City, Taiwan
| | - Jen-Fu Hsu
- Division of Pediatric Neonatology, Department of Pediatrics, Chang Gung Memorial Hospital, Linkou, Taoyuan, 33305, Taiwan. .,School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, 33302, Taiwan.
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, 33305, Taiwan. .,Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan. .,Department of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China. .,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, 518172, China.
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Lai PT, Lu WL, Kuo TR, Chung CR, Han JC, Tsai RTH, Horng JT. Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study. JMIR Med Inform 2019; 7:e14502. [PMID: 31769759 PMCID: PMC6913619 DOI: 10.2196/14502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/26/2019] [Accepted: 08/11/2019] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. OBJECTIVE As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. METHODS In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. RESULTS Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. CONCLUSIONS To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.
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Affiliation(s)
- Po-Ting Lai
- Department of Computer Science National Tsing Hua University, Hsinchu, Province of China Taiwan
| | - Wei-Liang Lu
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Ting-Rung Kuo
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Chia-Ru Chung
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Jen-Chieh Han
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Richard Tzong-Han Tsai
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Province of China Taiwan
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33
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Kim WY, Kang BJ, Chung CR, Park SH, Oh JY, Park SY, Cho WH, Sim YS, Cho YJ, Park S, Kim JH, Hong SB. Prone positioning before extracorporeal membrane oxygenation for severe acute respiratory distress syndrome: A retrospective multicenter study. Med Intensiva 2019; 43:402-409. [PMID: 29983197 PMCID: PMC10036879 DOI: 10.1016/j.medin.2018.04.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Revised: 04/05/2018] [Accepted: 04/29/2018] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To evaluate the clinical outcomes of patients with severe acute respiratory distress syndrome (ARDS) subjected to prone positioning before extracorporeal membrane oxygenation (ECMO). DESIGN A retrospective analysis of a multicenter cohort was carried out. SETTING Patients admitted to the Intensive Care Units of 11 hospitals in Korea. PATIENTS Patients were divided into those who underwent prone positioning before ECMO (n=28) and those who did not (n=34). INTERVENTIONS None. VARIABLES OF INTEREST Thirty-day mortality, ECMO weaning failure rate, mechanical ventilation weaning success rate, mechanical ventilation-free days at day 60. RESULTS The prone group had lower median peak inspiratory pressure and lower median dynamic driving pressure before ECMO. Thirty-day mortality was 21% in the prone group and 41% in the non-prone group (p=0.098). The prone group also showed a lower ECMO weaning failure rate, and a higher mechanical ventilation weaning success rate and more mechanical ventilation-free days at day 60. In the non-prone group, median dynamic compliance marginally decreased shortly after ECMO, but no significant change was observed in the prone group. CONCLUSIONS Prone positioning before ECMO was not associated to increased mortality and tended to exert a protective effect.
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Affiliation(s)
- W-Y Kim
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Pusan National University School of Medicine, Busan, Republic of Korea
| | - B J Kang
- Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea
| | - C R Chung
- Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - S H Park
- Department of Pulmonary and Critical Care Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul, Republic of Korea
| | - J Y Oh
- Division of Pulmonology and Critical Care Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
| | - S Y Park
- Department of Internal Medicine, Chonbuk National University Medical School, Jeonju, Republic of Korea
| | - W H Cho
- Department of Pulmonology and Critical Care Medicine, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Y S Sim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Hallym University Kangnam Sacred Heart Hospital, Seoul, Republic of Korea
| | - Y-J Cho
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - S Park
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - J-H Kim
- Department of Internal Medicine, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - S-B Hong
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Chung CR, Wang HY, Lien F, Tseng YJ, Chen CH, Lee TY, Liu TP, Horng JT, Lu JJ. Incorporating Statistical Test and Machine Intelligence Into Strain Typing of Staphylococcus haemolyticus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry. Front Microbiol 2019; 10:2120. [PMID: 31572327 PMCID: PMC6753874 DOI: 10.3389/fmicb.2019.02120] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 08/28/2019] [Indexed: 12/12/2022] Open
Abstract
Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.,Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan
| | - Frank Lien
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Yi-Ju Tseng
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.,Department of Information Management, Chang Gung University, Taoyuan City, Taiwan.,Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan City, Taiwan
| | - Chun-Hsien Chen
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.,Department of Information Management, Chang Gung University, Taoyuan City, Taiwan
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China
| | - Tsui-Ping Liu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan.,Department of Bioinformatics and Medical Engineering, Asia University, Taoyuan City, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.,College of Medicine, Chang Gung University, Taoyuan City, Taiwan.,Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan City, Taiwan
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35
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Chung CR, Kuo TR, Wu LC, Lee TY, Horng JT. Characterization and identification of antimicrobial peptides with different functional activities. Brief Bioinform 2019; 21:bbz043. [PMID: 31155657 DOI: 10.1093/bib/bbz043] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/20/2019] [Accepted: 03/20/2019] [Indexed: 02/28/2024] Open
Abstract
In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Ting-Rung Kuo
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Tzong-Yi Lee
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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36
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Kim WY, Kang BJ, Chung CR, Park SH, Oh JY, Park SY, Cho WH, Sim YS, Cho YJ, Park S, Kim JH, Hong SB. Prone positioning before extracorporeal membrane oxygenation for severe acute respiratory distress syndrome: A retrospective multicenter study. Med Intensiva 2018. [PMID: 29983197 DOI: 10.1016/j.medin.2018.04.013.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
Abstract
OBJECTIVE To evaluate the clinical outcomes of patients with severe acute respiratory distress syndrome (ARDS) subjected to prone positioning before extracorporeal membrane oxygenation (ECMO). DESIGN A retrospective analysis of a multicenter cohort was carried out. SETTING Patients admitted to the Intensive Care Units of 11 hospitals in Korea. PATIENTS Patients were divided into those who underwent prone positioning before ECMO (n=28) and those who did not (n=34). INTERVENTIONS None. VARIABLES OF INTEREST Thirty-day mortality, ECMO weaning failure rate, mechanical ventilation weaning success rate, mechanical ventilation-free days at day 60. RESULTS The prone group had lower median peak inspiratory pressure and lower median dynamic driving pressure before ECMO. Thirty-day mortality was 21% in the prone group and 41% in the non-prone group (p=0.098). The prone group also showed a lower ECMO weaning failure rate, and a higher mechanical ventilation weaning success rate and more mechanical ventilation-free days at day 60. In the non-prone group, median dynamic compliance marginally decreased shortly after ECMO, but no significant change was observed in the prone group. CONCLUSIONS Prone positioning before ECMO was not associated to increased mortality and tended to exert a protective effect.
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Affiliation(s)
- W-Y Kim
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Pusan National University School of Medicine, Busan, Republic of Korea
| | - B J Kang
- Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea
| | - C R Chung
- Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - S H Park
- Department of Pulmonary and Critical Care Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul, Republic of Korea
| | - J Y Oh
- Division of Pulmonology and Critical Care Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
| | - S Y Park
- Department of Internal Medicine, Chonbuk National University Medical School, Jeonju, Republic of Korea
| | - W H Cho
- Department of Pulmonology and Critical Care Medicine, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Y S Sim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Hallym University Kangnam Sacred Heart Hospital, Seoul, Republic of Korea
| | - Y-J Cho
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - S Park
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - J-H Kim
- Department of Internal Medicine, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - S-B Hong
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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37
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Ko JH, Müller MA, Seok H, Park GE, Lee JY, Cho SY, Ha YE, Baek JY, Kim SH, Kang JM, Kim YJ, Jo IJ, Chung CR, Hahn MJ, Drosten C, Kang CI, Chung DR, Song JH, Kang ES, Peck KR. Suggested new breakpoints of anti-MERS-CoV antibody ELISA titers: performance analysis of serologic tests. Eur J Clin Microbiol Infect Dis 2017; 36:2179-2186. [PMID: 28695355 PMCID: PMC7087918 DOI: 10.1007/s10096-017-3043-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 06/04/2017] [Indexed: 01/03/2023]
Abstract
To provide optimal cut-off values of anti-Middle East respiratory syndrome coronavirus (MERS-CoV) serologic tests, we evaluated performance of ELISA IgG, ELISA IgA, IFA IgM, and IFA IgG using 138 serum samples of 49 MERS-CoV-infected patients and 219 serum samples of 219 rRT-PCR-negative MERS-CoV-exposed healthcare personnel and patients. The performance analysis was conducted for two different purposes: (1) prediction of neutralization activity in MERS-CoV-infected patients, and (2) epidemiologic surveillance of MERS-CoV infections among MERS-CoV-exposed individuals. To evaluate performance according to serum collection time, we used ‘days post onset of illness (dpoi)’ and ‘days post exposure (dpex)’ assessing neutralization activity and infection diagnosis, respectively. Performance of serologic tests improved with delayed sampling time, being maximized after a seroconversion period. In predicting neutralization activity, ELISA IgG tests showed optimal performance using sera collected after 21 dpoi at cut-off values of OD ratio 0.4 (sensitivity 100% and specificity 100%), and ELISA IgA showed optimal performance using sera collected after 14 dpoi at cut-off value of OD ratio 0.2 (sensitivity 85.2% and specificity 100%). In diagnosis of MERS-CoV infection, ELISA IgG exhibited optimal performance using sera collected after 28 dpex, at a cut-off value of OD ratio 0.2 (sensitivity 97.3% and specificity 92.9%). These new breakpoints are markedly lower than previously suggested values (ELISA IgG OD ratio 1.1, sensitivity 34.8% and specificity 100% in the present data set), and the performance data help serologic tests to be practically used in the field of MERS management.
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Affiliation(s)
- J-H Ko
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea.,Division of Infectious Diseases, Department of Internal Medicine, Armed Forces Capital Hospital, Seongnam, South Korea
| | - M A Müller
- Institute of Virology, Charité - Universitätsmedizin Berlin, Helmut-Ruska-Haus Charitéplatz 1, 10117, Berlin, Germany.,German Centre for Infection Research, Braunschweig, Germany
| | - H Seok
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea
| | - G E Park
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea
| | - J Y Lee
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea
| | - S Y Cho
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea
| | - Y E Ha
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea
| | - J Y Baek
- Asia Pacific Foundation for Infectious Diseases (APFID), Seoul, Republic of Korea
| | - S H Kim
- Asia Pacific Foundation for Infectious Diseases (APFID), Seoul, Republic of Korea
| | - J-M Kang
- Division of Infectious Diseases, Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Y-J Kim
- Division of Infectious Diseases, Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - I J Jo
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - C R Chung
- Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - M-J Hahn
- Department of Molecular Cell Biology, Center for Molecular Medicine, Samsung Biomedical Research Institute, Sungkyunkwan University School of Medicine, Suwon, 440-746, South Korea
| | - C Drosten
- Institute of Virology, Charité - Universitätsmedizin Berlin, Helmut-Ruska-Haus Charitéplatz 1, 10117, Berlin, Germany.,German Centre for Infection Research, Braunschweig, Germany
| | - C-I Kang
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea
| | - D R Chung
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea.,Asia Pacific Foundation for Infectious Diseases (APFID), Seoul, Republic of Korea
| | - J-H Song
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea.,Asia Pacific Foundation for Infectious Diseases (APFID), Seoul, Republic of Korea
| | - E-S Kang
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - K R Peck
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Republic of Korea.
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38
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Jang GY, Kim YB, Wi H, Oh TI, Chung CR, Suh GY, Woo EJ. Imaging of regional air distributions in porcine lungs using high-performance electrical impedance tomography system. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:349-351. [PMID: 29059882 DOI: 10.1109/embc.2017.8036834] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Electrical impedance tomography (EIT) allows functional imaging of regional lung ventilation for real-time bedside monitoring of mechanically ventilated patients. Images showing time-changes of regional air distributions in the lungs can provide valuable diagnostic information for lung protective mechanical ventilation. This paper reports in vivo porcine imaging experiments of regional lung ventilation using a 16-channel parallel EIT system. Real-time time-difference chest images of 10 animals were reconstructed during mechanical ventilations with a temporal resolution of 50 frame/s. Analyzing the images together with the airway volume-pressure information from the mechanical ventilator, we could successfully produce regional compliance images at PEEP (positive end expiratory pressure) titration. From in vivo animal experiments, we propose the method as a continuous monitoring means for LPV (lung protective ventilation).
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39
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Park SJ, Lee KS, Kim SR, Min KH, Moon H, Lee MH, Chung CR, Han HJ, Puri KD, Lee YC. Phosphoinositide 3-kinase δ inhibitor suppresses interleukin-17 expression in a murine asthma model. Eur Respir J 2010; 36:1448-59. [PMID: 20351038 DOI: 10.1183/09031936.00106609] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Phosphoinositide 3-kinases (PI3Ks) contribute to the pathogenesis of asthma by regulating the activation of inflammatory mediators, inflammatory cell recruitment and immune cell function. Recent findings have indicated that PI3Ks also regulate the expression of interleukin (IL)-17, which has been recognised as an important cytokine involved in airway inflammation. In the present study, we investigated a role of PI3Kδ in the regulation of IL-17 expression in allergic airway disease using a murine model of asthma. After ovalbumin inhalation, administration of a selective p110δ inhibitor, IC87114, significantly attenuated airway infiltration of total cells, lymphocytes, neutrophils and eosinophils, as well as airway hyperresponsiveness, and attenuated the increase in IL-17 protein and mRNA expression. Moreover, IC87114 reduced levels of IL-4, -5 and -13, expression of keratinocyte chemoattractant protein and mRNA, and nuclear factor (NF)-κB activity. In addition, a NF-κB inhibitor, BAY 11-7085 substantially reduced the increase in IL-17 protein levels. Our results also showed that inhibition of IL-17 activity with an anti-IL-17 antibody remarkably reduced airway inflammation and hyperresponsiveness. These findings suggest that inhibition of the p110δ signalling pathway suppresses IL-17 expression through regulation of NF-κB activity and, thus, has therapeutic potential in asthma.
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Affiliation(s)
- S J Park
- Dept of Internal Medicine, Chonbuk National University Medical School, Deokjin-gu, Jeonju, Jeonbuk 561-180, South Korea
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40
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Abstract
This paper addresses the problem of geometry determination of a stereo rig that undergoes general rigid motions. Neither known reference objects nor stereo correspondence are required. With almost no exception, all existing online solutions attempt to recover stereo geometry by first establishing stereo correspondences. We first describe a mathematical framework that allows us to solve for stereo geometry, i.e., the rotation and translation between the two cameras, using only motion correspondence that is far easier to acquire than stereo correspondence. Second, we show how to recover the rotation and present two linear methods, as well as a nonlinear one to solve for the translation. Third, we perform a stability study for the developed methods in the presence of image noise, camera parameter noise, and ego-motion noise. We also address accuracy issues. Experiments with real image data are presented. The work allows the concept of online calibration to be broadened, as it is no longer true that only single cameras can exploit structure-from-motion strategies; even the extrinsic parameters of a stereo rig of cameras can do so without solving stereo correspondence. The developed framework is applicable for estimating the relative three-dimensional (3D) geometry associated with a wide variety of mounted devices used in vision and robotics, by exploiting their scaled ego-motion streams.
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Affiliation(s)
- F Dornaika
- Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China
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