Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides.
The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700), we have explored a variety of machine learning c...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 11/12; pp. 2397 - 2409 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153319075&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153319075 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2021 vid: 59 iid: 11/12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153319075 152987297 153319075 NLM34632545 10.1007/s11517-021-02443-6 NLM34632545 153319075 ppf: 2397 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides. aug: au: Wani, Mushtaq Ahmad Garg, Prabha Roy, Kuldeep K. affil: Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, 700054, Kolkata, West Bengal, India sug: subj: Probability Proteins Discriminant Analysis Clinical Assessment Tools ab: The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700), we have explored a variety of machine learning classifiers to build in silico models for AMP prediction, including Random Forest (RF), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), and ensemble learning. Among the various models generated, the RF classifier-based model top-performed in both the internal [Accuracy: 91.40%, Precision: 89.37%, Sensitivity: 90.05%, and Specificity: 92.36%] and external validations [Accuracy: 89.43%, Precision: 88.92%, Sensitivity: 85.21%, and Specificity: 92.43%]. In addition, the RF classifier-based model correctly predicted the known AMPs and non-AMPs; those kept aside as an additional external validation set. The performance assessment revealed three features viz. ChargeD2001, PAAC12 (pseudo amino acid composition), and polarity T13 that are likely to play vital roles in the antimicrobial activity of AMPs. The developed RF-based classification model may further be useful in the design and prediction of the novel potential AMPs. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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