LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data.

ABSTRACT:

Background

Identification of biomarkers, which are measurable characteristics of biological datasets, can be challenging. Although amplicon sequence variants (ASVs) can be considered potential biomarkers, identifying important ASVs in high-throughput sequencing datasets is challenging. Noise, algorithmic failures to account for specific distributional properties, and feature interactions can complicate the discovery of ASV biomarkers. In addition, these issues can impact the replicability of various models and elevate false-discovery rates. Contemporary machine learning approaches can be leveraged to address these issues. Ensembles of decision trees are particularly effective at classifying the types of data commonly generated in high-throughput sequencing (HTS) studies due to thei

SUBMITTER: Rudar J 

PROVIDER: S-EPMC8969335 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature