Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach

被引：0

作者：

Ming-Ren Yang

Yu-Wei Wu

机构：

[1] Taipei Medical University,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology

[2] National Taiwan University of Science and Technology,Department of Electrical Engineering

[3] Taipei Medical University Hospital,Clinical Big Data Research Center

来源：

BMC Bioinformatics | / 23卷

关键词：

Antimicrobial resistance; Pan-genome; Feature selection; eXtreme gradient boosting; XGBoost; Hypothetical proteins;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

引用

共 12 条

[1] Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach
Yang, Ming-Ren
Wu, Yu-Wei
BMC BIOINFORMATICS, 2022, 23 (SUPPL 4)
[2] Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach
Yang, Ming-Ren
Wu, Yu-Wei
BMC Bioinformatics, 2022, 23
[3] PARMAP: A Pan-Genome-Based Computational Framework for Predicting Antimicrobial Resistance
Li, Xuefei
Lin, Jingxia
Hu, Yongfei
Zhou, Jiajian
FRONTIERS IN MICROBIOLOGY, 2020, 11
[4] A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains
Her, Hsuan-Lin
Wu, Yu-Wei
BIOINFORMATICS, 2018, 34 (13) : 89 - 95
[5] Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC)
Yang, Ming-Ren
Su, Shun-Feng
Wu, Yu-Wei
FRONTIERS IN GENETICS, 2023, 14
[6] Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection
Shi, Jinhong
Yan, Yan
Links, Matthew G.
Li, Longhai
Dillon, Jo-Anne R.
Horsch, Michael
Kusalik, Anthony
BMC BIOINFORMATICS, 2019, 20 (Suppl 15)
[7] Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection
Jinhong Shi
Yan Yan
Matthew G. Links
Longhai Li
Jo-Anne R. Dillon
Michael Horsch
Anthony Kusalik
BMC Bioinformatics, 20
[8] A Cross-Validated Feature Selection (CVFS) approach for extracting the most parsimonious feature sets and discovering potential antimicrobial resistance (AMR) biomarkers
Yang, Ming-Ren
Wu, Yu-Wei
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2023, 21 : 769 - 779
[9] Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant Acinetobacter baumannii from whole genome sequencing and gene expression
Jia, Huiqiong
Li, Xinyang
Zhuang, Yilu
Wu, Yuye
Shi, Shasha
Sun, Qingyang
He, Fang
Liang, Shanyan
Wang, Jianfeng
Draz, Mohamed S.
Xie, Xinyou
Zhang, Jun
Yang, Qing
Ruan, Zhi
ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, 2024, 68 (12)
[10] Enhancing the one health initiative by using whole genome sequencing to monitor antimicrobial resistance of animal pathogens: Vet-LIRN collaborative project with veterinary diagnostic laboratories in United States and Canada
Olgica Ceric
Gregory H. Tyson
Laura B. Goodman
Patrick K. Mitchell
Yan Zhang
Melanie Prarat
Jing Cui
Laura Peak
Joy Scaria
Linto Antony
Milton Thomas
Sarah M. Nemser
Renee Anderson
Anil J. Thachil
Rebecca J. Franklin-Guild
Durda Slavic
Yugendar R. Bommineni
Shipra Mohan
Susan Sanchez
Rebecca Wilkes
Orhan Sahin
G. Kenitra Hendrix
Brian Lubbers
Deborah Reed
Tracie Jenkins
Alma Roy
Daniel Paulsen
Rinosh Mani
Karen Olsen
Lanny Pace
Martha Pulido
Megan Jacob
Brett T. Webb
Sarmila Dasgupta
Amar Patil
Akhilesh Ramachandran
Deepanker Tewari
Nagaraja Thirumalapura
Donna J. Kelly
Shelley C. Rankin
Sara D. Lawhon
Jing Wu
Claire R. Burbick
Renate Reimschuessel
BMC Veterinary Research, 15

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