AI-DRIVEN COMPARATIVE GENOMIC INSIGHTS INTO MICROBIAL STRAINS FOR DOMESTIC WASTEWATER TREATMENT IN THE INDIAN CONTEXT.
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Keywords
Microbial genomics; Bioremediation; Domestic wastewater; Artificial intelligence; Machine learning; Pseudomonas; Bacillus; Chlorella
Abstract
Aim- The gift examine pursuits to assess the ability of microbial bioremediation for home wastewater remedy in India the usage of comparative genomic techniques, with an emphasis on the mixture of synthetic intelligence (AI) and machine getting to know (ML) device. Methodology: Relevant literature and publicly to be had genomic datasets have been analysed to assess key microbial genera worried in wastewater treatment, which includes Pseudomonas, Bacillus, Phanerochaete, Aspergillus and Chlorella. Genomic capabilities related to pollutant degradation, strain tolerance and environmental adaptability have been tested, and modern AI/ML-primarily based methods for genome mining, useful annotation and microbial consortium design have been reviewed. Results: Comparative genomic evaluation suggests that bacterial, fungal and algal taxa possess wonderful yet complementary genetic developments permitting the degradation of natural pollution, vitamins, dyes and heavy metals normally observed in home wastewater. AI- and ML-assisted analyses enhance the prediction of biodegradation ability and facilitate the rational desire of sturdy microbial consortia perfect to various Indian wastewater situations. Interpretation: The integration of comparative genomics with AI-pushed analytical frameworks affords an powerful technique for optimizing microbial bioremediation tactics. This method facilitates the improvement of green, sustainable and decentralized wastewater remedy systems tailor-made to Indian environmental settings.
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References
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