Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Sung-Yoon | - |
dc.contributor.author | Kim, Mira | - |
dc.contributor.author | Bae, Ji-Eun | - |
dc.contributor.author | Bang, Iel-Soo | - |
dc.contributor.author | Lee, Sang-Woong | - |
dc.date.accessioned | 2022-10-12T06:40:08Z | - |
dc.date.available | 2022-10-12T06:40:08Z | - |
dc.date.created | 2022-09-22 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85653 | - |
dc.description.abstract | Several pathogens that spread through the air are highly contagious, and related infectious diseases are more easily transmitted through airborne transmission under indoor conditions, as observed during the COVID-19 pandemic. Indoor air contaminated by microorganisms, including viruses, bacteria, and fungi, or by derived pathogenic substances, can endanger human health. Thus, identifying and analyzing the potential pathogens residing in the air are crucial to preventing disease and maintaining indoor air quality. Here, we applied deep learning technology to analyze and predict the toxicity of bacteria in indoor air. We trained the ProtBert model on toxic bacterial and virulence factor proteins and applied them to predict the potential toxicity of some bacterial species by analyzing their protein sequences. The results reflect the results of the in vitro analysis of their toxicity in human cells. The in silico-based simulation and the obtained results demonstrated that it is plausible to find possible toxic sequences in unknown protein sequences. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000851843100001 | - |
dc.identifier.doi | 10.3390/s22176557 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.17 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85137545621 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 17 | - |
dc.contributor.affiliatedAuthor | Ahn, Sung-Yoon | - |
dc.contributor.affiliatedAuthor | Lee, Sang-Woong | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | protein | - |
dc.subject.keywordAuthor | toxin | - |
dc.subject.keywordAuthor | virulence factors | - |
dc.subject.keywordAuthor | BERT | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.