Error Correction in Long-Read Metagenomic Sequences Using Hybrid ML Approaches

Metagenomics › Machine Learning Metagenome Assembly Research

Error Correction in Long-Read Metagenomic Sequences Using Hybrid ML Approaches

Interns will create machine learning models to identify and correct sequencing errors in long-read technologies (PacBio, Oxford Nanopore) used in metagenomic studies. They will integrate short-read alignments and deep learning to improve consensus accuracy for downstream assembly and annotation tasks.

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