Alexandra Geada, Dhaval Jivanji, Daniel M Tennenbaum, Armin Ghomeshi, Raghuram Reddy, Michael Sencaj, Jamie Thomas, Daniel E Nassau, Ranjith Ramasamy
Long-term impact of commonly performed operations in pediatric urology on reproductive and sexual health
Therapeutic advances in urology
groundwater gw quality monitoring vital sustainable water resource management present study introduced metagenomederived machine learning ml model aimed enhancing predictive understanding diagnostic interpretation gw pollution associated petroleum framework taxonomic metabolic profiles derived gw metagenomes combined use input dataset employing strategies optimized data integration model selection parameter tuning achieved significant increase diagnostic accuracy petroleumpolluted gw explanatory artificial intelligence techniques identified petroleum degradation pathways rhodocyclaceae strong predictors pollution diagnosis metagenomic analysis corroborated presence gene operons encoding aminobenzoate xylene biodegradation within de novo assembled genome rhodocyclaceae genomecentric metagenomic analysis thus clarified ecological interactions associated microbiomes breaking petroleum contaminants validating mlbased diagnostic results metagenomederived ml framework enhances predictive diagnosis petroleum pollution also offers interpretable insights interaction microbiomes petroleum proposed ml framework demonstrates great promise use sciencebased strategy onsite monitoring remediation gw pollution