Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbial Bioremediation of Plastics01:28

Microbial Bioremediation of Plastics

Polyethylene terephthalate (PET) is a synthetic polymer widely utilized in the packaging industry, particularly for bottles and containers. Due to its chemical stability and durability, PET accumulates in the environment, contributing significantly to plastic pollution. It comprises repeating units of terephthalic acid and ethylene glycol, resulting in a semi-crystalline structure that is resistant to natural degradation processes.A notable breakthrough in plastic biodegradation came with the...
Microbial Bioremediation of Hydrocarbons01:26

Microbial Bioremediation of Hydrocarbons

Bioremediation is an environmentally sustainable process that employs living organisms—primarily microorganisms—to degrade or neutralize pollutants from contaminated environments. In oil spills and hydrocarbon pollution, bioremediation involves the use of hydrocarbon-degrading bacteria to transform toxic compounds into less harmful substances. This approach leverages natural microbial metabolic processes and is considered both cost-effective and ecologically favorable compared to physical or...
Microbial Bioremediation of Pesticides01:28

Microbial Bioremediation of Pesticides

Pesticides often feature structurally complex chemical architectures, incorporating halogen groups and multiple aromatic rings. These characteristics confer high chemical stability, rendering many pesticides resistant to natural degradation processes. This resistance poses significant environmental concerns, as persistent pesticide residues can accumulate in ecosystems and affect non-target organisms.Despite the inherent stability of many pesticides, certain microorganisms possess the metabolic...
Bioremediation00:46

Bioremediation

Bioremediation is the use of prokaryotes, fungi, or plants to remove pollutants from the environment. This process has been used to remove harmful toxins in groundwater as a byproduct of agricultural run-off and also to clean up oil spills.
Bioplastics01:27

Bioplastics

Bioplastics derived from microbial processes present a sustainable alternative to conventional petroleum-based plastics. Among these, polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrates (PHBs), have emerged as prominent candidates due to their biodegradability and biocompatibility. These polymers are synthesized by a variety of bacteria, such as Cupriavidus necator and Pseudomonas putida, which naturally accumulate PHAs as intracellular carbon and energy reserves, especially under...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Looking beyond Sorption in GAC Filters: How Extended Contact Times and Functionally Distinct Microbial Biomass Enable Enhanced Micropollutant Biodegradation.

Environmental science & technology·2026
Same author

Comment on "By integrating previously overlooked drivers AI boosts bioaccumulation assessment in fish".

Journal of hazardous materials·2026
Same author

Feature-weighted maximum representative subsampling.

Scientific reports·2026
Same author

60 Years of Persistence Science in ES&T: From Foundational Insights to Demonstrated Impact.

Environmental science & technology·2026
Same author

Hazard Assessment of Antioxidants as Contaminants of Concern.

Environmental science & technology letters·2026
Same author

Persistence Assessment of Chemicals: Trajectories toward New Approach Methodologies (P-NAMs).

Environmental science & technology·2026

Related Experiment Video

Updated: Jun 16, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach.

Jörg Wicker1, Kathrin Fenner, Lynda Ellis

  • 1Institut für Informatik/I12, Technische Universität München, D-85748 Garching b. München, Germany.

Bioinformatics (Oxford, England)
|January 29, 2010
PubMed
Summary

This study introduces a hybrid approach for predicting organic pollutant biodegradation pathways, enhancing the University of Minnesota Pathway Prediction System (UM-PPS) with machine learning and domain knowledge for accurate probability estimates.

Related Experiment Videos

Last Updated: Jun 16, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Area of Science:

  • Environmental chemistry
  • Computational toxicology
  • Biotechnology

Background:

  • Current biodegradation pathway prediction methods lack domain knowledge and probability estimates.
  • Organic environmental pollutants pose significant risks, necessitating accurate biodegradation prediction.
  • The University of Minnesota Pathway Prediction System (UM-PPS) is a key tool in this field.

Purpose of the Study:

  • To develop a hybrid knowledge- and machine learning-based approach for predicting biodegradation products and pathways.
  • To integrate probability estimates into the biodegradation prediction process.
  • To improve the accuracy and reliability of the UM-PPS.

Main Methods:

  • Developed a hybrid system combining domain knowledge with machine learning.
  • Implemented relative reasoning within a machine learning framework.
  • Assigned probability estimates to biotransformation rules.

Main Results:

  • Achieved approximately 0.8 recall and precision for 13 transformation rules using leave-one-out cross-validation.
  • Demonstrated the ability to optimize precision without compromising recall.
  • Currently integrating results into an experimental version of the UM-PPS server.

Conclusions:

  • The hybrid approach effectively enhances biodegradation pathway prediction.
  • Probability estimates allow for practical management of the recall-precision trade-off.
  • The improved UM-PPS shows promise for environmental pollutant analysis.