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Updated: Jun 25, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
Published on: June 17, 2012
Phytobial remediation advances and application of omics and artificial intelligence: a review
Indica Mohan1,2, Babita Joshi3, Deepak Pathania1,2
1Department of Environmental Sciences, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India.
Abstract:
Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation and microbial association are emerging as a potential method for removing heavy metals and other contaminants from soil. The treatment uses plant physiology and metabolism to remove or clean up various soil contaminants efficiently. In recent years, omics and artificial intelligence have been seen as powerful techniques for phytobial remediation. Recently, AI and modeling are used to analyze large data generated by omics technologies. Machine learning algorithms can be used to develop predictive models that can help guide the selection of the most appropriate plant and plant growth-promoting rhizobacteria combination that is most effective at remediation. In this review, emphasis is given to the phytoremediation techniques being explored worldwide in soil contamination.
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