An Improved Machine Learning-Based Approach to Assess the Microbial Diversity in Major North Indian River Ecosystems.

ABSTRACT: The rapidly evolving high-throughput sequencing (HTS) technologies generate voluminous genomic and metagenomic sequences, which can help classify the microbial communities with high accuracy in many ecosystems. Conventionally, the rule-based binning techniques are used to classify the contigs or scaffolds based on either sequence composition or sequence similarity. However, the accurate classification of the microbial communities remains a major challenge due to massive data volumes at hand as well as a requirement of efficient binning methods and classification algorithms. Therefore, we attempted here to implement iterative K-Means clustering for the initial binning of metagenomics sequences and applied various machine learning algorithms (MLAs) to classify the newly identified unknown mi

SUBMITTER: Choudhury N 

PROVIDER: S-EPMC10218686 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature