Pilot study: Machine learning-assisted identification of phylum-level microbiome signatures across advanced wastewater treatment stages

Xudong Liu; Karna Ramachandraiah; Gabi Hitti; Qingyu Zhou; Alya Limayem

doi:10.1016/j.jece.2026.124048

Pilot study: Machine learning-assisted identification of phylum-level microbiome signatures across advanced wastewater treatment stages

Xudong Liu
, Karna Ramachandraiah
, Gabi Hitti
, Qingyu Zhou
, Alya Limayem

School of Computing

University of North Florida
University of South Florida Health
University of South Florida, Tampa

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	124048
Journal	Journal of Environmental Chemical Engineering
Volume	14
Issue number	5
DOIs	https://doi.org/10.1016/j.jece.2026.124048
State	Published - Oct 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation

ASJC Scopus Subject Areas

Chemical Engineering (miscellaneous)
General Chemical Engineering
Environmental Science (miscellaneous)
Waste Management and Disposal
Pollution
General Engineering
Process Chemistry and Technology

Keywords

16S rRNA gene sequencing
Data-driven analysis
Machine learning, Wastewater treatment
Microbiome signatures
Phylum-level microbiome
Wastewater monitoring

Access to Document

10.1016/j.jece.2026.124048

Cite this

@article{544c431b0af649f99a8ae5c9204ea4a7,

title = "Pilot study: Machine learning-assisted identification of phylum-level microbiome signatures across advanced wastewater treatment stages",

keywords = "16S rRNA gene sequencing, Data-driven analysis, Machine learning, Wastewater treatment, Microbiome signatures, Phylum-level microbiome, Wastewater monitoring",

author = "Xudong Liu and Karna Ramachandraiah and Gabi Hitti and Qingyu Zhou and Alya Limayem",

note = "Publisher Copyright: {\textcopyright} 2026 Elsevier Ltd.",

year = "2026",

month = oct,

doi = "10.1016/j.jece.2026.124048",

language = "English",

volume = "14",

number = "5",

}

TY - JOUR

T1 - Pilot study

T2 - Machine learning-assisted identification of phylum-level microbiome signatures across advanced wastewater treatment stages

AU - Liu, Xudong

AU - Ramachandraiah, Karna

AU - Hitti, Gabi

AU - Zhou, Qingyu

AU - Limayem, Alya

PY - 2026/10

Y1 - 2026/10

KW - 16S rRNA gene sequencing

KW - Data-driven analysis

KW - Machine learning, Wastewater treatment

KW - Microbiome signatures

KW - Phylum-level microbiome

KW - Wastewater monitoring

UR - https://www.scopus.com/pages/publications/105045437718

UR - https://www.scopus.com/pages/publications/105045437718#tab=citedBy

U2 - 10.1016/j.jece.2026.124048

DO - 10.1016/j.jece.2026.124048

M3 - Article

AN - SCOPUS:105045437718

SN - 2213-2929

VL - 14

JO - Journal of Environmental Chemical Engineering

JF - Journal of Environmental Chemical Engineering

IS - 5

M1 - 124048

ER -

Pilot study: Machine learning-assisted identification of phylum-level microbiome signatures across advanced wastewater treatment stages

UN SDGs

ASJC Scopus Subject Areas

Keywords

Access to Document

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