Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra.

ABSTRACT: Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models

SUBMITTER: Migdadi L 

PROVIDER: S-EPMC9213235 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature