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Machine learning classification of quorum sensing-induced bacterial aggregation using flow rate assays on paper chips toward bacterial species identification in potable water sources.

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Title: Machine learning classification of quorum sensing-induced bacterial aggregation using flow rate assays on paper chips toward bacterial species identification in potable water sources.
Authors: Choi SJ; Department of Biosystems Engineering, The University of Arizona, Tucson, AZ, 85721, United States; Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea., Lee MH; Division of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongsangbuk-do, 37673, Republic of Korea., Liang Y; Department of Chemistry and Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States., Lin EC; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States., Khanthaphixay B; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States., Leigh PJ; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States., Hwang DS; Division of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongsangbuk-do, 37673, Republic of Korea; Institute for Convergence Research and Education in Advanced Technology, Yonsei University International Campus I-CREATE, Incheon, 21983, Republic of Korea. Electronic address: dshwang@postech.ac.kr., Yoon JY; Department of Biosystems Engineering, The University of Arizona, Tucson, AZ, 85721, United States; Department of Chemistry and Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States. Electronic address: jyyoon@arizona.edu.
Source: Biosensors & bioelectronics [Biosens Bioelectron] 2025 Sep 15; Vol. 284, pp. 117563. Date of Electronic Publication: 2025 May 07.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Advanced Technology Country of Publication: England NLM ID: 9001289 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4235 (Electronic) Linking ISSN: 09565663 NLM ISO Abbreviation: Biosens Bioelectron Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-4235
DOI:10.1016/j.bios.2025.117563