Literature DB >> 33222377

Enhancement of microbiome management by machine learning for biological wastewater treatment.

Wenfang Cai1,2, Fei Long3, Yunhai Wang2, Hong Liu3, Kun Guo1.   

Abstract

Here, we propose to develop microbiome-based machine learning models to predict the response of biological wastewater treatment systems to environmental or operational disturbances or to design specific microbiomes to achieve a desired system function. These machine learning models can be used to enhance the stability of microbiome-based biological systems and warn against the failure of these systems.
© 2020 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

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Year:  2020        PMID: 33222377      PMCID: PMC7888473          DOI: 10.1111/1751-7915.13707

Source DB:  PubMed          Journal:  Microb Biotechnol        ISSN: 1751-7915            Impact factor:   5.813


  14 in total

1.  A meta-analysis of the microbial diversity observed in anaerobic digesters.

Authors:  Michael C Nelson; Mark Morrison; Zhongtang Yu
Journal:  Bioresour Technol       Date:  2010-12-03       Impact factor: 9.642

2.  Outlining microbial community dynamics during temperature drop and subsequent recovery period in anaerobic co-digestion systems.

Authors:  Leticia Regueiro; Marta Carballa; Juan M Lema
Journal:  J Biotechnol       Date:  2014-12-20       Impact factor: 3.307

Review 3.  Microbial management of anaerobic digestion: exploiting the microbiome-functionality nexus.

Authors:  Marta Carballa; Leticia Regueiro; Juan M Lema
Journal:  Curr Opin Biotechnol       Date:  2015-02-13       Impact factor: 9.740

4.  Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

Authors:  Wenfang Cai; Keaton Larson Lesnik; Matthew J Wade; Elizabeth S Heidrich; Yunhai Wang; Hong Liu
Journal:  Biosens Bioelectron       Date:  2019-03-13       Impact factor: 10.618

Review 5.  Deciphering microbial community robustness through synthetic ecology and molecular systems synecology.

Authors:  Ben Stenuit; Spiros N Agathos
Journal:  Curr Opin Biotechnol       Date:  2015-04-14       Impact factor: 9.740

Review 6.  Microbial interactions: from networks to models.

Authors:  Karoline Faust; Jeroen Raes
Journal:  Nat Rev Microbiol       Date:  2012-07-16       Impact factor: 60.633

7.  Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms.

Authors:  Luguang Wang; Fei Long; Wei Liao; Hong Liu
Journal:  Bioresour Technol       Date:  2019-12-02       Impact factor: 9.642

8.  Microbial Community Predicts Functional Stability of Microbial Fuel Cells.

Authors:  Keaton Larson Lesnik; Wenfang Cai; Hong Liu
Journal:  Environ Sci Technol       Date:  2019-12-16       Impact factor: 9.028

Review 9.  Microbiology and potential applications of aerobic methane oxidation coupled to denitrification (AME-D) process: A review.

Authors:  Jing Zhu; Qian Wang; Mengdong Yuan; Giin-Yu Amy Tan; Faqian Sun; Cheng Wang; Weixiang Wu; Po-Heng Lee
Journal:  Water Res       Date:  2015-12-17       Impact factor: 11.236

Review 10.  Common principles and best practices for engineering microbiomes.

Authors:  Christopher E Lawson; William R Harcombe; Roland Hatzenpichler; Stephen R Lindemann; Frank E Löffler; Michelle A O'Malley; Héctor García Martín; Brian F Pfleger; Lutgarde Raskin; Ophelia S Venturelli; David G Weissbrodt; Daniel R Noguera; Katherine D McMahon
Journal:  Nat Rev Microbiol       Date:  2019-09-23       Impact factor: 60.633

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  2 in total

1.  Big data and computational advancements for next generation of Microbial Biotechnology.

Authors:  Zulema Udaondo
Journal:  Microb Biotechnol       Date:  2021-10-29       Impact factor: 5.813

2.  The future of Microbial Biotechnology.

Authors:  Lawrence P Wackett
Journal:  Microb Biotechnol       Date:  2021-10-06       Impact factor: 5.813

  2 in total

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